Useful Chemistry

MiniSymposium Bradley Lab 2011

2011-10-05T12:47:00.000-04:00

I recently presented a 15 minute summary of the current research in my lab on September 29, 2011 at the Drexel University Department of Chemistry Faculty Mini-Symposium. The main project discussed was the Open Melting Point Collection done in collaboration with Andrew Lang and Antony Williams. Work by Evan Curtin is also shown, demonstrating the application of melting point and solubility in reaction design. I'll discuss this imine synthesis project in more detail later but Evan's experiments are listed in the notebook.

MiniSymp2011 Bradley

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Patrick Ndungu talk at Drexel on Nanotechnology

2011-08-18T15:04:00.000-04:00

One of my former Ph. D. students, Patrick Ndungu (now at University of KwaZulu Natal, South Africa) will be speaking at Drexel University on Friday August 19, 2011 at 12:30 in Disque 109.

Some Interesting Perspectives on the Integration of Nanomaterials with Energy and Water Treatment Technologies

As part of various key concerns in a developing economy, clean energy and access to potable water are an integral part of most strategic visions for sustainable socio-economic development. Of particular interest is the search for greener energy solutions that includes R&D into hydrogen energy technologies, and devices that utilize solar energy. Whilst clean water concerns centre on indigenous, cost-effective, and relatively simple technologies that can be easily deployed in remote or off-grid areas. Within this framework, this presentation will look at the evolution of a select body of work that has focused on the integration of carbon Nanomaterials into systems for hydrogen storage, fuel cells, and photo-catalytic materials for water treatment.

Google Apps Scripts Workshop at Drexel University

2011-08-17T15:59:00.001-04:00

Andrew Lang will be in Philadelphia next week and we will be running a workshop on Leveraging Google Spreadsheets with Scripts for Research and Teaching. Now that our institution is no longer providing Microsoft Office for students in the fall term, it seems like a good time to explore converting some assignments and projects relying on Excel to freely available Google Spreadsheets. (Resources available here)

Andrew Lang (Department of Mathematics at Oral Roberts University) and Jean-Claude Bradley (Department of Chemistry at Drexel University) will host a workshop on Google Apps Scripts from 10:30 to 12:00 on Tuesday August 23, 2011 at the Hagerty Library in room L13C. They will demonstrate how users with no programming experience can easily add functions and drop-down menus to a Google Spreadsheet. Some chemistry examples will be detailed, such as inter-converting compound identifiers (common name, SMILES, CAS number, etc.) and reporting properties (melting points, solubility, density, etc.) with a single click. Participants are encouraged to suggest applications in other fields to explore during the workshop.

Open Melting Point Collection Book Edition 1

2011-08-11T16:02:00.000-04:00

Several months of work through a collaboration between myself, Andrew Lang, Antony Williams and Evan Curtin have culminated in the publication of an Open Melting Point Collection Book. Like our other books on solubility and Reaction Attempts, the conversion from a database format to a PDF has several advantages.

Now that the book has been accepted by Nature Precedings, it provides a convenient mechanism for citation via DOI, a formal author list, version control, etc. The book is also now available from LuLu.com either as a free PDF download or a physical copy. Because the book runs 699 pages (it covers 2706 unique compounds) the lowest price we could get is $30.96, which just covers printing and shipping.

Even though we have melting points for about 20,000 unique compounds, most of these are from single sources. Unless we can get another major donation of melting points (not using any of the sources we already have), progress in curating single values manually will take time.

As described in the abstract:

This book represents a PDF version of Dataset ONSMP029 (2706 unique compounds, 7413 measurements) from a project to collect and curate melting points made available as Open Data. This particular collection was selected from the application of a threshold to favor the likelihood of reliability. Specifically, the entire range of averaged values for a data point was set to 0.01 C to 5 C, with at least two different measurements within this range. Measurements were pooled and processed from the following sources: Alfa Aesar, MDPI, Bergstrom, PhysProp, DrugBank, Bell, Oxford MSDS, Hughes, Griffiths and the Chemical Information Validation Spreadsheet. Links to all the information sources and web services are available from the Open Melting Point Resource page: http://onswebservices.wikispaces.com/meltingpoint

This filtering of double validated melting point measurements within a range of 5C is an attempt to provide a "reasonably" good source, It is imperative to understand that this is not a "trusted source" - as I've mentioned several time there is no such thing. However, since absolute trusted sources do not exist, this double validated dataset of 2706 compounds is probably the best we can do for now. In fact, use of this double validated to build melting point model has led to some excellent models, which are far superior to models constructed from the entire database of 20,000 compounds.

Rapid analysis of melting point trends and models using Google Apps Scripts

2011-07-19T21:53:00.000-04:00

I recently reported on how Google Apps Scripts can be used to facilitate the recording and calculations associated with a chemistry laboratory notebook. (also see resource page)

I will demonstrate here how these scripts can be used to rapidly discover trends in the melting points of analogs for the curation of data and the evaluation of models. The two melting point services that Andrew Lang created under the gONS menu were used to keep track of the measured and predicted melting points for all reactants and product as part of a "dashboard view" of the reaction being performed.

For looking at melting point trends, the following template sheet can be used.

For reasons explained previously, the template sheet has no active scripts in the page (except for the images). These are just the values generated from running the scripts corresponding to the column headings on the common names. In order to use for another series of compounds just make a copy of the entire Google Spreadsheet (File->Make a Copy) then enter the new list and pick the desired script to run from the menus. Once the values are computed remember to copy and paste as values.

It is important to understand that our melting point service is not a "trusted source" - it simply reports the average of all recorded data sources, ignoring values marked as DONOUSE. That means that not all data points are equal and it is up to the user to determine a threshold of some type to decide how to use a particular data point.

In this investigation, I have marked in green averaged experimental values where at least 3 different values are clustered within a few degrees. A link in column H is automatically generated from the CSID to provide a very convenient way to evaluate the data sources. For example the link for methanol has 3 very close but different melting point values: -98 C, -97.6 C and -97.53 C. The -98 C value is repeated 7 times because this resulted from the automatic merging of several Open Collections.

In general we don't manually add values that are identical from different sources because it is likely that these all originate from the same source. We have to make that assumption because proper data provenance is usually lacking in chemical information sources today. A Google search will often return the same one or two melting points from dozens of sites, which may turn out to be an outlier when compared with other independent sources. (CAS numbers are generated in the template sheet because they are useful for searching Google for melting points - for example see here for methanol)

In another scenario where there are 3 or more different but close values and a few clear marked outliers, I considered these averages as having passed my threshold and colored these green as well. A good example is ethanol, which I have previously used to illustrate our curation method.

It turns out that for the series of n-alcohols from methanol to 1-decanol, I was able to mark in green every experimental melting point average, making the confidence level of the following plot about as high as it can get from current chemical information sources.

It is particularly gratifying to note that the predicted melting points based on Andrew Lang's random forest Model002 perform very well here, even predicting a melting point minimum at 3 carbons. Note that this model is Open Source and uses Open Descriptors derived from the CDK. It does not yet include the results of our most recent curation efforts. Any new models incorporating improved datasets will be listed here.

Extending the analysis to n-alkyl carboxylic acids from formic acid to decanoic acid provides the following plot, with the same confidence for the experimental averages.

For this series, the random forest model not only predicts that the lowest melting point is for the 5 carbon analog but it also appears to take the shape of a zig-zag pattern, especially for the first 6 acids. Since this alternating pattern has been attributed to the way that carboxylic acid dimer bilayers pack in 3D (Bond2004), it is hard to imagine how simple 2D descriptors from the CDK can predict this. We will have to investigate this in more detail.

More generally, molecular symmetry can greatly affect the melting point via the way that crystals pack in 3D (see Carnelley's Rule, Brown2000). At some point we would like to incorporate this factor in our models. The current model should not be able to make predictions based on symmetry or stereochemistry.

We can also explore the melting point patterns of cyclic systems. Going from cyclopropane to cyclohexane there is a large jump from a 5 to a 6 membered ring and this is roughly reflected in the model:

Cycloalkanones behave similarly to cycloalkanes, showing a jump from 5 to 6 membered rings which agrees well with the model going from cyclobutanone to cyclohexanone:

However, in going from methylcylopropane to methylcyclohexane, the model diverges substantially from experimental results. It does start to get harder to find corroborating melting points and only 2 values can be found for methylcyclobutane.

Going from cyclopropanecarboxylic acid to cyclohexanecarboxylic acid shows a U-type pattern and is not well matched by the model. However, there is additional uncertainty about the melting point of cyclopentanecarboxylic acid.

For the series from cyclopropylamine to cyclohexylamine, there initially appears to be a significant mismatch between the model and experiment. However, because we have retained the provenance information in the spreadsheet it becomes clear that the cyclobutylamine number (in the orange square below) comes from a single source. There is actually a good match between the other 3 values. However, as demonstrated here, there has not been enough information on when the model is reliable to assign the source of the discrepancy at this point.

These examples show that provenance information is a critical dimension in the analysis of trends in melting point data. The Google Apps Scripts and associated Google Spreadsheet template presented here offer a quick and convenient way to provide access to both averaged values and a way of assessing confidence in an averaged value. Performing these tasks manually is generally too time-consuming to encourage researchers to follow such a practice. This is perhaps the reason that the current peer-review process accepts a single "trusted source" in analyses of this kind, even though such a practice inevitably leads to mis-interpretations and errors that cascade through the scientific literature.

Practical Tips on using Google Apps Scripts for Chemistry Applications

2011-07-14T14:52:00.001-04:00

A few weeks ago I described our use of Google Apps Scripts, developed by Rich Apodaca and Andrew Lang, as an intuitive interface to information related to a chemistry laboratory notebook. Since then we have been using these tools to actively plan and record experiments (e.g. UC-EXP269) and we have learned their strengths and weaknesses.

The most problematic aspect of Google Apps Scripts running within Google Spreadsheets turns out to be the way caching and refreshing operate. There does not appear to be an obvious way to refresh a single cell. So if a script times out or fails, Google stores that failed output on their servers and will not run it again until some time has elapsed (which seems to be on the order of about an hour). Typing in a new input for that cell will cause the script to run again but entering a previously entered input will only retrieve the cached output, even a failed output. For example, if you have a cell calculating the MW from "benzene" entered in another cell and the script fails for any reason, typing in "ethanol" will get it to run again for the new input, but going back to "benzene" will just pull up the cached output of "Failed".

Nevertheless, I did come across some tricks to force a refresh indirectly. If you insert a row or column then re-enter the desired scripts in the new cells, they will run again. You simply need to then delete the old column with failed outputs. This is fine for simple sheets but it can be a headache for sheets that have several calculation dependencies between cells.

To avoid these complications, simply refresh the entire sheet by duplicating it, deleting the old sheet and then renaming the new one to the original name. The problem now is that it will refresh all the cells, not just those that had failed outputs. And if there are a large number of scripts on that sheet the odds are good that at least one will fail on that particular attempt, especially if several are hitting the same web server.

As a result of all these problems, I would not recommend using these services as I had initially hoped, where a researcher would enter data into a template sheet loaded with scripts to automatically generate a series of calculated outputs. There is a way to achieve this end but it requires thinking about the scripts in a slightly different way.

As I mentioned above, there are tricks for refreshing an entire sheet or a column or row. In order to avoid re-running the scripts that already returned desired outputs, we need to lock them in. This can be done by highlighting the completed cells, copying them (either control-c or Edit->Copy) then pasting them as values (from the Edit menu). Now refreshing will only be done on the cells with failed outputs and these can be locked in as well as soon as they complete.

The downside of this approach is that you lose the information about which script was run to generate the output values. And to change an input requires re-selecting the desired script. But in practice it is so convenient to hit a dropdown menu and hit getMW (for example) that this downside is quite minimal, especially when contrasted with the upside of knowing that others will see your information reliably, independent of how the services are running at a particular time.

Over the past few weeks we have found that some services fail more often than others and it would be advantageous to have some redundancies. This has been particularly problematic for the cactus services recently, which we often use for resolving common names. By using ChemSpiderIDs (CSIDs), the cactus services can be bypassed for several of the gONS services. So a good practice for any application is to generate and lock in SMILES and CSIDs right away from the common name. CAS numbers can be used too but the gChem service that Rich has created sometimes yields multiple CAS numbers and these will fail as input for a subsequent script.

We now have a chemistry Google Apps Scripts spreadsheet to keep track of which inputs are allowed for all the available services, along with information about the output, creator and description. We also keep track of requests and plans for new scripts, marked as "pending" under the status field.

Surprisingly, pasting images "as values" within a Google Spreadsheet cell does not ensure that they will appear consistently - often the cells are just blank upon loading. This makes the idea of using an embedded sheet to display reaction schemes within a wiki lab notebook page not practical. However, using the scripts and a template to generate the scheme by just typing the name, SMILES or CSID for the reactants and product is a very efficient way to generate a consistent look for schemes within a notebook. It only requires a final step of taking the image of the screen and cropping using Paint. For example, here is a scheme thus generated for UC-EXP269.

Taking into account all of these factors, the reaction template sheet we provide does not have by default any scripts running within cells (except for the images). However, it is set up to quickly adapt to other reactions for planning amounts of reactants (by weight or volume), calculating concentrations, yields, melting points (experimental and predicted), solubilities, links to ChemSpider, 2D rendering of structures (including full schemes) and links to interactive NMR spectra using ChemDoodle. It simply requires users to hit one of the 3 drop-down menus (gChem, gCDK or gONS) and select the appropriate script for a particular cell.

Even if the user does not want to use this particular reaction template it still makes sense to make a copy of the template sheet because it is an easy way to copy all of the necessary Google Script without opening the editor.

Open Notebook Science Talk at HUBbub 2011

2011-07-01T09:09:00.000-04:00

On April 6, 2011 I presented at the HUBzero Conference in Indianapolis on "Open Notebook Science: Does Transparency Work?".

This presentation will first describe Open Notebook Science, the practice of making the laboratory notebook and all associated raw data available to the public in real time. Examples of current applications in organic chemistry - solubility and chemical reactions - will be detailed. Key details of the current technical implementation will be described and possible applicability to nanotechnology projects will be explored. Finally, the implications for Intellectual Property protection, claims of priority, subsequent publication in peer reviewed journals and the eventual automation of the scientific process will be explored.

The organizers did a great job in making the recording available as either a video or audio podcast.

I learned a great deal at the conference about how researchers from various fields use the HUBzero software to manage and share their data. As described on their website:

HUBzero® is a platform used to create dynamic web sites for scientific research and educational activities. With HUBzero, you can easily publish your research software and related educational materials on the web.

Although the system is not primarily designed for completely Open sharing, I did get the impression that for some applications there was significant interest in making data and processes more Open. There is certainly an enthusiastic user community around HUBzero - check out the recordings for some of the other talks here.

Open Notebook Science HUBzero 2011

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The 4-benzyltoluene melting point twist

2011-06-22T19:56:00.000-04:00

Evan Curtin and I were in the lab this morning to follow up on our effort to curate the melting point of 4-benzyltoluene. I identified the next step to confirm an upper limit of -15 C:

With the information available thus far from our experiments (UC-EXP266), we think it is unlikely that the +4.6 C value can be correct because we observed no solidification after 2 days at -15 C. The patent reports that solidification of some viscous mixtures took up to a full week but we did not observe an appreciable increase in viscosity for 4-benzyltoluene at -15 C. But in order to be sure we will first freeze the sample again below -40 C and let it warm up to -15 C in the freezer and confirm that it melts completely.

But when we took the sample out of the freezer after 16 days it was completely frozen!

This now effectively ruled out the -30 C value and re-opened the possibility that the +4.6 C value could be the best estimate. Learning from our previous failed attempt to observe a temperature plateau when heating the sample, this time we let it warm as slowly as possible by leaving it in an ice water bath inside of a Styrofoam container. This worked much better as the sample warmed a few degrees over several hours. This time Evan observed a clear transition from the solid to the liquid phase in the 4-6 C range.(UC-EXP266)

The curation record for the melting point of 4-benzyltoluene now looks like this:

When I introduce the concept of Open Notebook Science in my talks I usually make the point that there are no facts - just measurements embedded within assumptions.

The 4-benzyltoluene melting point story is a really good example of this principle. When I stated that I thought that "it is unlikely that the +4.6 C value can be correct because we observed no solidification after 2 days at -15 C", it was not the measurement that was in error - it was the interpretation. And when new information came to light, an experiment was proposed to either challenge or further support that interpretation. There were never any "facts" in this story (nor is the +4.6 C value a "fact" from these results).

I think that this is how science functions best and most efficiently. Unfortunately we don't usually have access to all pertinent raw measurements, assumptions and interpretations. I would be extremely interested in seeing how the -30 C value was determined. This is actually the value provided by the company that sold us this batch of material (as well as the PhysProp entry in the image above). Because of slow crystallization, I can see how this could happen if the temperature was dropped until solidification was observed. In our observations, the -30 C to -35 C range is roughly where we observed rapid solidification upon cooling. (UC-EXP266)

Google Apps Scripts for an intuitive interface to organic chemistry Open Notebooks

2011-06-18T12:46:00.001-04:00

Rich Apodaca recently demonstrated how Google Apps Scripts can be added to Google Spreadsheets to enable simple calling of web services for chemistry applications (gChem). Although we have been using web service calls from within a Google spreadsheet for some time (solubility calculation by NMR link #3 and misc chem conversions link #1), the process wasn't as intuitive as it could be because one had to find then paste lengthy urls.

Rich's approach enables simply clicking the desired web service from a menu on Google Spreadsheets and these functions have simple names like getSMILES. Andrew Lang has now added several web services from our ONS projects and the CDK. There are now 3 menus to choose from: gChem, gCDK and gONS.

To demonstrate the power of these tools consider the rapid construction of a customized interface to an experiment in a lab notebook (in this example UC-EXP263).

1) Because Andy has added a gONS service to render images of molecules from ChemSpider, consistent reaction schemes can now be constructed from this template by simply typing the name of the reactants and products then embedding in the wiki.

2) Planning of the reaction to calculate reactant amounts and product yield can then be processed by simply typing the name of the chemicals. Services calling molecular weight and density are automatic based on the chemical name as input.

3) Typing the name of the solvent then allows easy access to the solubility properties of the reaction components. The calculated concentrations of the reactants and product can be directly compared with their measured maximum solubility. In this experiment the observed separation of the product from the solution is consistent with these measurements.

4) Both experimental and predicted melting points (using Model002) can then be lined up for comparison. A large discrepancy between the two would flag a possible error - in this case good agreement is found. Noting that the product's melting point is near room temperature (53 C) explains why two layers were were observed to form during the course of the reaction and cooling to 0 C induced the product to precipitate. Links to the melting measurements are also provided in column N for easy exploration.

5) Column O provides a quick link to the ChemSpider entries for all compounds and column P provides links to the Reaction Attempts Explorer where, for example, one can explore other reactions where the product was involved. Finally columns Q and R provide one click access to an interactive NMR spectrum of the product, powered by ChemDoodle.

The last few columns still use our older code to call web services but over time these should be added to the gONS collection for convenience.

The easiest way to experiment with this interface is probably to just make a copy (File -> Make a Copy from the Google Spreadsheet menu). The sheet can then be customized for other applications.

Live Tweeting Haumea: the Open Science Ratchet at work?

2011-06-16T14:43:00.001-04:00

Eugenie Samuel Reich just announced on the Nature NewsBlog that astronomer Mike Brown live-tweeted his observations of a transit of dwarf planet Haumea by its moon, Namaka.

About a year ago, I wrote about Mike Brown and the controversy about the discovery of Haumea stemming from a competitor's more aggressive data dissemination practice. In that post I speculated that we could expect accelerated data sharing over time due to the Open Science Ratchet, where the actions of scientists that are most open set the pace for everyone else working on that particular project, regardless of their views on how secretive science should be.

I don't know if Mike Brown has changed his views on data sharing - or if he has always felt this way but thought it was too risky until now. Either way, he certainly is taking the lead at this point to demonstrate how radical openness can be done in astronomy!

image from Wikipedia entry for Haumea

My talk at SLA on Trust in Science and Open Melting Point Collections

2011-06-16T11:28:00.001-04:00

On June 14 and 15, 2011 I attended the Special Libraries Association conference and made presentations on two panels on the role of trust in science with a case-study of the Open Melting Point collections that Andrew Lang, Antony Williams and I have been assembling and curating.

The first panel was on the "International Year of Chemistry: Perils and Promises of Modern Communication in the Sciences". My colleague Laurence Souder from the Department of Culture and Communications at Drexel presented on "Trust in Science and Science by Blogging", using as an example the NASA press release on arsenic replacing phosphorus in bacteria and subsequent controversy taking place in the blogosphere. (see post in Scientific American blog today)

Watch Lawrence Souder's presentation screencast and slides.

The second panel was on "New Forms of Scholarly Communications in the Sciences". Don Hagen from the National Technical Information Service presented on "NTIS Focus on Science and Data: Open and Sustainable Models for Science Information Discovery" and Dorothea Salo discussed the evolving role of libraries and institutional repositories on scholarly communication and archiving.

Watch Don Hagen's presentation screencast and slides.

My own slides and screencast from the second panel are available below:

Bradley SLA Talk on Open Melting Point Collections

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More on 4-benzyltoluene and the impact of melting point data curation and transparency

2011-06-11T21:59:00.003-04:00

There are many motivations for performing scientific research. One of these is the desire to advance public scientific knowledge.

This is a difficult concept to quantify or even qualitatively assess. One can try to use literature citations and impact factors but that captures only a small fraction of the true scientific impact. For example, one formal citation of our solubility dataset doesn't represent the 100,000 anonymous solubility queries made directly to our database. And of these the actual impact will depend on exactly how the information was used. Egon Willighagen has identified this as a problem for the Chemistry Development Kit (CDK) as well: many more people use the CDK than reflected simply by the number of citations to the original paper.

There are a few of us who believe that curating chemistry data is a high impact activity. Antony Williams spends a considerable amount of time on this activity and frequently uncovers very serious errors from a number of data sources. Andrew Lang and I have put in a similar effort in collecting and curating solubility measurements openly - and recently (with Antony) we have been doing the same for melting points.

Although attempting to estimate the total impact of the curation activity isn't really practical, we can look at a specific and representative example to capture the scope.

I recently exposed the situation with the melting point measurements of 4-benzyltoluene. In brief, the literature provided contradictory information that could not be resolved without performing an experiment. Although an exact measurement was not found, a limit was determined that ruled out all measurements except for one.

Ironically it turns out that the melting point of this compound is its most important property for industrial use! Derivatives of diphenylmethane were sought out to replace PCBs as electrical insulating oils for capacitors because of toxicity concerns. As described in this patent (US5134761), for this application one requires the oil to remain liquid down to -50 C. Another key requirement is the ability to absorb hydrogen gas liberated at the electrode surface (a solubility property). Since this is optimal for smaller alkyl groups on the rings, it places benzyltoluene isomers at the focal point of research for this application.

The patent states: "According to references, the melting points of the position isomers of benzyltoluenes are as follows..." but does not make a specific reference. However, by comparing the numbers with other sources we can presume that the reference is the Lemneck1954 paper I discussed previously.

The patent then uses these melting points to calculate the melting behavior of mixtures of these isomers, as they obtain without further purification from a Friedel-Crafts reaction.

If our results are correct and the melting point of 4-benzyltoluene is not +4.6 C but well below -15 C, then the calculated properties in the patent may be significantly in error as well. With the information available thus far from our experiments (UC-EXP266), we think it is unlikely that the +4.6 C value can be correct because we observed no solidification after 2 days at -15 C. The patent reports that solidification of some viscous mixtures took up to a full week but we did not observe an appreciable increase in viscosity for 4-benzyltoluene at -15 C. But in order to be sure we will first freeze the sample again below -40 C and let it warm up to -15 C in the freezer and confirm that it melts completely.

It is in light of this analysis that I make the case that open curation of melting point data is likely to be a high impact activity relative to the amount of time required to perform it. The problem is that errors such as these cascade through the scientific record and likely retard scientific progress by causing confusion and wasted effort. Consider the total cost in terms of research and legal fees for just one patent. As I discussed previously, consider the effect of compromised and contradictory data now known to exist within training sets on the pace of developing reliable melting point models (cascading down to solubility models dependent upon melting point predictions or measurements - and ultimately cascading to the efficiency of drug design).

It is important to note that the benefits of curation would be greatly diminished without the component of transparency. We are not claiming to provide a "trusted source" of melting point data. There is no such thing - and operating under the illusion of the trusted source model has resulted in the mess we are in now - with multiple melting point values for the same compound cascading and multiplying to different databases (a good and still unresolved example is benzylamine).

What we are doing is reporting all the sources we can use and marking some sources as DONOTUSE so they are not included in the calculation of the average - with an explanation. We never delete data so users can make informed choices and not be in a position of having to trust our judgement. If someone does not agree with me that failure to freeze after 2 days at -15 C does not necessarily rule out the +4.6 C value for the melting point for 4-benzyltoluene then they are free to use it.

Using a trusted source model, all values within a collection are equally valid. In the transparency model not all values are equal - we are justifiably more confident in a melting point value near -114 C for ethanol than for a melting point with a single source (like this compound).

And finally, an important factor for having an impact on science is discoverability. It is likely that someone doing research involving the melting behavior of 4-benzyltoluene would perform at least quick Google search. What they are likely to find is not just a simple number without provenance but rather a collection of results capturing the full subtlety of the situation under discussion. This is a natural outcome of working transparently.

Open Melting Points on iPhone via MMDS

2011-06-10T14:32:00.000-04:00

As Alex Clark explained on his blog Cheminformatics 2.0, both predicted and experimental melting points from our Open Data collection are now available on iPhones via his MMDS webservices protocol.

Although the app is not free, the web service (#7 from our collection) that Andrew Lang and Alex created for this purpose is Open and available for anyone to use. It reads an XML formatted molfile and returns the average measured melting point, predicted melting point, SMILES, CSID and a link to the ChemSpider entry.

The quest to determine the melting point of 4-benzyltoluene

2011-06-09T19:57:00.000-04:00

I recently reported that we are attempting to curate the open melting point measurements collected from multiple sources such as Alfa Aesar, PhysProp (EPIsuite) and several smaller collections. I mentioned that some values - like benzylamine - simply don't converge and the only way to resolve the issue is to actually get a high purity sample and do a measurement.

Since that report, we found another non-converging situation with 4-benzyltoluene. As shown below, reported measurements range from -30 C to 125C.

The values in red have been removed from the calculation of the average based on evidence we obtained from ordering the compound from TransWorld Chemicals and observing its behavior when exposed to various temperatures. The details can be found from UC-EXP266 (which I performed with Evan Curtin).

Immediately after opening the package it was clear that the compound was a liquid and thus the 125C and 98.5C values became improbable enough to remove.

First Evan Curtin and I dropped the still sealed bottle into an ice bath (0C) and after 10 minutes there was no trace of solidification.

At this point, this does not necessarily rule out the values near 5C because of the short time in the bath.

We then used an acetone/dry ice bath and did see a rapid and clear solidification after reaching -30C to -35C.

Letting the bath temperature rise it was difficult to tell what was happening but there seemed to be some liquefaction around -12C.

In order to get a more precise measurement, we transferred about 2 mls of the sample into a test tube and introduced the thermometer directly in contact with the substance. After quickly freezing the contents in a dry ice/acetone bath, the sample was removed and its behavior was observed over time, as shown below.

I was expecting to see the internal temperature rise then plateau at the melting point until all the solid disappeared and then finally observe a second temperature rise. This comes from experience in making 0C baths within minutes by simply throwing ice into pure water.

As shown above that is not at all what happened. The liquid formed gradually starting at about -9C and never reached a plateau even up to +7C, where there was still much solid left.

If we look at the method used to generate the 4.58 C value (Lamneck1954) we find that a similar method was cited - but not actually described there. The actual curves are not available either. However, this paper provides melting points for several compounds within a series, which is often useful for spotting possible errors - unless of course these are systematic errors. In this particular case it doesn't help much because the 2-methyl derivative is similar but the 3-methyl analogue is very close to -30 C value listed in our sources.

Notice that one of the "melting points" (3-methyldicyclohexylmethane) is not even measurable because it forms a glass. It is easy to see how melting points below room temperature can generate very different values - and very difficult to assess if the full experimental details of the measurements are not reported.

Trying to get at more details lets look at the referenced paper (Goodman1950). Indeed the researchers determine the melting point by plotting the temperature over time as the sample is heated and looking for a plateau. The obvious difference is that the heating rate is about an order of magnitude slower than in our experiment.
This paper also highlights the fact that there are more twists and turns in the melting point story. One compound (2-butylbiphenyl) was found to have 2 melting points that can be observed by seeding with different polymorphic crystals.

At this point, our objective of obtaining an actual melting point was replaced with trying to at least mark a reasonably confident upper limit. After leaving the sample at -15 C in a freezer for two days, no solidification was observed - not even an appreciable increase in viscosity. For this reason, all melting point values above -15C were removed from the calculation of the average and show up in red.

With only the -30 C measurement left, this is now the default value for 4-benzyltoluene - until further experimentation.

More Open Melting Points from EPI and other sources: on the path to ultimate curation

2011-05-25T21:11:00.000-04:00

As recently as 2008, Hughes et al published a paper asking: Why Are Some Properties More Difficult To Predict than Others? A Study of QSPR of Solubility, Melting Point, and Log P

The question then is: why do QSPR models consistently perform significantly worse with regard to melting point? In the Introduction, we proposed three reasons for the failure of QSPR models: problems with the data, the descriptors, or the modeling methods. We find issues with the data unlikely to be the only source of error in Log S, Tm, and Log P predictions. Although the accuracy of the data provides a fundamental limit on the quality of a QSPR model, we attempted to minimize its influence by selecting consistent, high quality data... With regards to the accuracy of Tm and Log P data, both properties are associated with smaller errors than Log S measurement. Moreover, the melting point model performed the worst, yet it is by far the most straightforward property to measure...We suggest that the failure of existing chemoinformatics descriptors adequately to describe interactions in the crystalline solid phase may be a significant cause of error in melting point prediction.

Indeed, I have often heard that melting point prediction is notoriously difficult. This paper attempted to discover why and suggested that it is more likely that the problem is related to a deficiency in available descriptors rather than data quality. The authors seem to argue that taking a melting point is so straightforward that the resulting dataset is almost self-evidently high quality.

I might have thought the same before we started collecting melting point datasets.

It turns out that validating melting points can be very challenging and we have found enormous errors - even cases where the same compound in the same dataset is assigned very different melting points. Under such conditions it is mathematically impossible to obtain high correlations between predicted and "measured" values.

Since we have no additional information to go on (no spectral proof of purity, reports of heating rate, observations of melting behavior, etc.) the only way we can validate data points is to look for strong convergence from multiple sources. For example, consider the -130 C value for the melting point of ethanol (as discussed previously in detail). It is clearly an outlier from the very closely clustered values near -114 C.

This outlier value is now highlighted in red to indicate that it was explicitly identified to not be used in calculating the average. Andrew Lang has now updated the melting point explorer to allow a convenient way to select or deselect outliers and indicate a reason (service #3). For large separate datasets - such as the Alfa Aesar collection - this can be done right on the melting point explorer interface with a click. For values recorded in the Chemical Information Validation sheet, one has to update the spreadsheet directly.

This is the same strategy that we used for our solubility data - in that case by marking outliers with "DONOTUSE". This way, we never delete data so that anyone can question our decision to exclude data points. Also by not deleting data, meaningful statistical analyses of the quality of currently available chemical information can be performed for a variety of applications.

The donation of the Alfa Aesar dataset to the public domain was instrumental in allowing us to start systematically validating or excluding data points for practical or modeling applications. We have also just received confirmation that the entire EPI (PhysProp) melting point dataset can be used as Open Data. Many thanks to Antony Williams for coordinating this agreement and for approval and advice from Bob Boethling at the EPA and Bill Meylan at SRC.

In the best case scenario, most of the melting point values will quickly converge as in the ethanol case above. However, we have also observed cases where convergence simply doesn't happen.

Consider the collection of reported melting points for benzylamine.

One has to be careful when determining how many "different" values are in this collection. Identical values are suspicious since they may very well originate from the same ultimate source. Convergence for the ethanol value above is credible because most of the values are very close but not completely identical, suggesting truly independent measurements.

In this case values actually diverge into sources of either +10 C, - 10 C, -30 C or about -45 C. If you want to play the "trusted source" game, do you trust more the Sigma-Aldrich value at +10C or the Alfa Aesar value at -43 C?

Lets try looking at the peer-reviewed literature. A search on SciFinder gives the following ranges:

The lowest melting point listed there is the +10C value we already have in our collection but these references are to other databases. The lowest value from a peer-reviewed paper is 37-38 C.

This is strange because I have a bottle of benzylamine in my lab and it is definitely a liquid. Investigating the individual references reveals a variety of errors. In one, benzylamine is listed as a product but from the context of the reaction it should be phenylbenzylamine:

(In a strange co-incidence the actual intermediate - benzalaniline - is the imine that Evan Curtain has synthesized recently in order to measure its solubility)

In another example, the melting point of a product is incorrectly associated with the reactant benzylamine:

The erroneous melting points range all the way up to 280 C and I suspect that many of these are for salts of benzylamine, as I reported previously for the strychnine melting point results from SciFinder.

With no other obvious recourse from the literature to resolve this issue, Evan attempted to freeze a sample of benzylamine from our lab.(UC-EXP265)

Unfortunately, the benzylamine sample proved to be too impure (<85% by NMR) and didn't solidify even down to -78 C. We'll have to try again from a much more pure source. It would be useful to get reports from a few labs who happen to have benzylamine handy and provide proof of purity by NMR and a pic to demonstrate solidification.

As most organic chemists will attest, amines are notorious for appearing as oils below their melting points in the presence of small amounts of impurities. I wonder if the divergence of melting points in this case is due to this effect. By providing NMR data from various samples subjected to freezing, it might be possible to quantify the effect of purity on the apparent freezing point. I think the images of the solidification are also important because I think that some may mistake very high viscosity with actual formation of a solid. At -78 C we observed the sample to exhibit a viscosity similar to that of syrup.

Our model predicts a melting point of about -38 C for benzylamine and so I suspect that the values of -43 C and -46 C are most likely to be close to the correct range. Lets find out.

La Science par Cahier de Laboratoire Ouvert à l'Acfas

2011-05-10T10:33:00.001-04:00

On May 9, 2011 I presented remotely for the French-Canadian Association for the Advancement of Science (ACFAS). This was the first time I gave a talk about Open Notebook Science in French. In fact the last time I gave a scientific talk in French was probably in 1995, when I was doing a postdoc at the Collège de France in Paris. I remember being teased for my French Canadian accent back then so happily that wasn't an issue this time. Even though I was a bit rusty I think I managed to communicate the key points well enough. (At least I hope I did)

My presentation was a good fit for the theme of the conference: Une autre science est possible : science collaborative, science ouverte, science engagée, contre la marchandisation du savoir. (Another Science is possible: collaborative science, open science, against the commercialization of knowledge). I would like to thank the organizers (Mélissa Lieutenant-Gosselin and Florence Piron) for inviting me to participate.

I was able to record most of the talk (see below) but very near the end Skype decided to install an update and shut down so the recording ends somewhat abruptly. Given what people use Skype for, that default setting for updates really doesn't make much sense.

La Science a Cahier de Laboratoire Ouvert

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Breast Cancer Coalition talk on ONS and Taxol solubility

2011-05-08T16:28:00.000-04:00

On May 1, 2011 I presented "Accelerating Discovery by Sharing: a case for Open Notebook Science" at the National Breast Cancer Coalition Annual Advocacy Conference in Arlington, VA. This was the first year where they had a session on an Open Science related theme and the organizers invited me to highlight some of the tools and practices in chemistry which might be applicable to cancer research.

I was really touched by the passion from those in the audience as well as the other speakers and conference participants I met afterward. For many, their deep connection with the cause was strongly rooted in a personal experience as breast cancer survivors themselves or their loved ones. Several expressed a frustration with the current system of sharing results from scientific studies. They felt that knowledge sharing is much slower than it needs to be and that potentially useful "negative" results are generally not disclosed at all.

The NBCC has ambitiously set 2020 as the deadline to end breast cancer (including a countdown clock). It seems reasonable to me that encouraging transparency in research is a good strategy to accelerate progress. Of course, great care must be exercised wherever patient confidentiality is a factor. But health care researchers are already experienced with following protocols to anonymize datasets for publication. Opting to work more openly would not change that but it might affect when and how results are shared. Also there is a great deal of science related to breast cancer that does not directly involve human subjects.

One initiative that particularly impressed me was The Susan G. Komen for the Cure Tissue Bank, presented by Susan Clare from Indiana University and moderated by Virginia Mason from the Inflammatory Breast Cancer Research Foundation. As a result of this effort, thousands of women have donated healthy breast tissue to create a comprehensive database richly annotated with donor genetics and medical history. The idea of trying to tackle a disease state by first understanding normal functioning in great detail was apparently somewhat of a paradigm shift for the cancer research community and it was challenging to implement. According to Dr. Clare, data from the Tissue Bank have shown that the common practice of using apparently unaffected tissue adjacent to a tumor as a control may not be valid.

This example highlights one of the key principles of Open Science: there is value in everyone knowing more - even if it isn't immediately clear how that knowledge will prove to be useful.

In my experience, this is a fundamental point that distinguishes those who are likely to favor Open Science from those who reject its value. If two researchers are discussing Open Science and only one of them views this philosophy as being self-evident the conversation will likely be about why someone would want (or not want) to share more and the focus will fall on extrinsic motivators such as academic credit, intellectual property, etc. If both researchers view this philosophy as self-evident the conversation will probably gravitate towards how and what to share.

I refer to this philosophy as being self-evident because I don't think people can become convinced through argumentation (I've never seen that happen). Within the realm of Open Notebook Science I have been involved in countless discussions about the value of sharing all experimental details - even when errors are discovered. I can think of a few ways in which this is useful - for example telegraphing a research direction to those in the field or providing data for researchers who study how science is actually done (such as Don Pellegrino). But even if I couldn't think of a single application I believe that there is value in sharing all available data.

A good example of this philosophy at work is the Spectral Game. Researchers who uploaded spectral data to ChemSpider as Open Data did not anticipate how their contribution would be used. They didn't do it for extrinsic motives such as traditional academic credit. Assuming that their motivation was similar to our group's, they did it because they believed it was an obviously useful thing to do. It is only much later - after a critical mass of open spectra were collected - that the idea arose to create a game from the dataset.

With this mindset, I explored what contribution we might make to breast cancer research by performing a phrase search strategy. Doing a simple Google search for "breast cancer" solubility generated mainly two types of results.

The first set involve the solubility behavior of biomolecules within the cellular environment. An example would be the observed increased solubility of gamma-tubulin in cancerous cells.
The second type of results address the difficulty in preparing formulations for cancer drugs due to solubility problems. A good example of this is Taxol (paclitaxel), where existing excipients are not completely satisfactory - in the case of Cremophor EL some patients experience a hypersensitivity.
Since our modeling efforts thus far have focused on non-aqueous solubility, there is possibly an opportunity to contribute by exploring the solubility behavior of paclitaxel. By inputting solubility data from a paper by Singla 2002 into our solubility database, Abraham descriptors for paclitaxel are automatically calculated and the solubilities in over 70 solvents are predicted.

In addition, by simply adding the melting point of paclitaxel, we automatically predict its solubility at any temperature where these solvents are liquids (see for example water).

Because of the way we expose our results to the web, a Google search for "paclitaxel solubility acetonitrile" now returns the actual value in the Google summary on the first page of results (currently 7th on the first page). The other hits have all 3 keywords somewhere in the document but one has to click on each link then perform a search within the document to find out if the acetonitrile solubility for paclitaxel is actually reported. (Note that clicking on our link ultimately takes you to the peer-reviewed paper with the original measurement.)

To be clear about what we are doing here - we are not claiming to be the first to predict the solubility of paclitaxel in these solvents using Abraham descriptors or any other method. Nor are we claiming that we have directly made a dent in the formulation problem of paclitaxel. We are not even indicating that we have done a thorough search of the literature - that would take a lot more time than we have had given the enormous amount of work on paclitaxel and its derivatives.

All we are doing is fleshing out the natural interface between the knowledge space of the UsefulChem/ONS Challenge projects and that of breast cancer research - AND - we are exposing the results of that intersection through easily discoverable channels. By design, these results are exposed as self-contained "smallest publishable units" and they are shared as quickly (and as automatically) as possible. The traditional publication system does not have mechanism to disseminate this type of information. (Of course when enough of these are collected and woven into a narrative that fits the criteria for a traditional paper they can and should be submitted for peer-reviewed publication).

Here is a scenario for how this could work in this specific instance. A graduate student (who has never heard of Open Science or UsefulChem, the ONS Challenge, etc.) is asked to look for new formulations for paclitaxel (or other difficult to solubilize anti-cancer agents). They do a search on commercial databases offered by their university for various solubilities of paclitaxel and cannot find a measurement for acetonitrile. They then do a search on Google and find a hit directly answering their query, as I detailed above. This leads them to our prediction services and they start using those numbers in their own models.

That is a good outcome - and that is exactly what has been happening (see the gold nanodot paper and the phenanthrene soil contamination study as examples). But the real paydirt would come from the graduate student recognizing that we've done a lot of work collecting measurements and building models for solubility and melting points, and contact us about a collaboration. As long as they are comfortable with working openly we would be happy actively work together.

I'm using the formulation of paclitaxel as an example but I'm sure that there are many more intersections between solubility and breast cancer research. With a bit of luck I hope we can find a few researchers who are open to this type of collaboration.

As another twist to this story, I will briefly mention here too that Andrew Lang has started to screen our Ugi product virtual library for docking with the site where paclitaxel binds to gamma-tubulin (D-EXP018). This might shed some light on some much cheaper alternatives to the extremely expensive paclitaxel and derivatives. The drug binds through 3 hydrogen bonds, shown below - rendered in 2D and 3D representations (obtained from the PDB ligand viewer)

The slides and recording of my talk are embedded below:

NBCC Open Notebook Science Talk

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Collaboration using Open Notebook Science in Academia book chapter

2011-05-08T11:37:00.000-04:00

I am very pleased to report that the book chapter that I co-wrote with Andrew Lang, Steve Koch and Cameron Neylon is now available online: Collaboration using Open Notebook Science in Academia. This is the 25th chapter of Collaborative Computational Technologies for Biomedical Research, edited by Sean Ekins, Maggie Hupcey, Antony Williams and Alpheus Bingham.

Our chapter provides some fairly detailed examples of how Open Notebook Science can be used to enhance collaboration between researchers from both similar or distant fields. It also suggests certain paths towards machine/human collaboration in science. Hopefully it will encourage researchers who have an interest in Open Science to experiment with some of the tools and strategies mentioned.

I am also grateful to Wiley for choosing our chapter as the free online sample for the book!

This book discusses the state-of-the-art collaborative and computing techniques for the pharmaceutical industry, the present and future implications and opportunities to advance healthcare research. The book tackles problems thoroughly, from both the human collaborative and the data and informatics side, and is very relevant to the day-to-day activities running a laboratory or a collaborative R&D project. It can be applied to help organizations make critical decisions about managing drug discovery and development partnership. The book follows a “man- methods-machine” format with sections on how to get people to collaborate, collaborative methods, and computational tools for collaboration. This book offers the reader a “getting started guide” or instruction on “how to collaborate” for new laboratories, new companies, and new partnerships, as well as a user manual for how to troubleshoot existing collaborations.

Evan Curtin is the May 2011 RSC ONS Challenge Winner

2011-05-07T10:23:00.001-04:00

Evan Curtin, a chemistry freshman student working under the supervision of Jean-Claude Bradley at Drexel University, is the May 2011 Royal Society of Chemistry Open Notebook Science Challenge Award winner. He wins a cash prize from the RSC.

Evan's primary focus has centered on synthesizing aromatic imines and measuring their solubility in a number of organic solvents. This will allow us to generate Abraham descriptors for this class of compounds in order to predict their solubility in 70+ solvents. Coupled with our new model to include temperature dependent solubility, this should greatly facilitate optimal solvent prediction for this and related reactions.

Imine formation is of particular interest to the UsefulChem group because it is the first step of the Ugi reaction, which we have used to synthesize compounds with anti-malarial activity. But it is also a simple convenient reaction in itself to test our Solvent Selector's ability to predict optimal conditions (solvent and temperature) for isolation of products by precipitation.

Evan's synthesis experiments are available here:
http://usefulchem.wikispaces.com/Exp263
http://usefulchem.wikispaces.com/Exp262
http://usefulchem.wikispaces.com/Exp261

and his solubility experiments are listed here:
http://onschallenge.wikispaces.com/Exp207
http://onschallenge.wikispaces.com/Exp206
http://onschallenge.wikispaces.com/Exp205
http://onschallenge.wikispaces.com/Exp204
http://onschallenge.wikispaces.com/Exp201
http://onschallenge.wikispaces.com/Exp198
http://onschallenge.wikispaces.com/Exp197

Three more RSC ONS Awards will be made during 2011. Submissions from students in the US and the UK are still welcome.
For more information see:
http://onschallenge.wikispaces.com
http://onschallenge.wikispaces.com/RSCAwards2010

ACS and ACRL presentations on web services and trust in science

2011-04-04T14:50:00.001-04:00

Update: the recording of my ACS talk on Rapid Dissemination of Chemical Information for people and machines using Open Notebook Science is now available here.

On March 30 and 31, 2011 I presented two related talks - the first remotely for the American Chemical Society (ACS) Meeting and the second in Philadelphia at the meeting for the Association of College and Research Libraries (ACRL).

In the ACS talk "Rapid Dissemination of Chemical Information for people and machines using Open Notebook Science", I spoke for the first time in detail about the results of the open modeling Andrew Lang and I carried out on the open dataset of melting points we collected starting with the Alfa Aesar dataset recently made public.

We used Skype and Google Presenter with the help of Peter Murray-Rust on site at the conference and it went fairly well I think. Henry Rzepa had a good question about polymorphism possibly being responsible for different melting points from various sources. I don't think that is the problem in most of these cases but we can certainly spend some time investigating the reports of polymorphism for cases where the information is available. One of the big problems is that we don't know the history of the sample used for a melting point from most sources like chemical vendor sites. At least in journal articles we might be told which solvent was used to crystallize the sample. If multiple sources agree on a certain melting point and there is one outlier, I think it is reasonable to assume that the common melting point is likely to correspond to the thermodynamically favored polymorph. This might not be correct in all cases but - without the means to discover more information about the sample histories - I think it makes sense to proceed in this way. Since we don't consider polymorphism in our modeling, there is an implicit assumption that - in the case of polymorphism - we are dealing with the thermodynamically most stable form.

My ACRL talk "Is there a role for Trust in Science?" focused more on the Chemical Information Validation study and outcomes. There were several good questions at the end. One particularly good comment addressed my speculation that within a few years, the open models in most of the useful chemical spaces will be sufficiently good that it will be as easy to Google a melting point or a solubility as it is now to get driving directions. The question was: weren't we just replacing trust from one information source to another, namely these models. I don't think the concept of trust applies in these cases because the training sets, the descriptors and the performance of the models are (and will be) open. This is in sharp contrast with most commercial software generating predictions for solubility and melting points - these are generally black boxes because either the training set, the model or the descriptors are not open.

Open Notebook Science Web Services - ACS Spring 2011

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ACRL Trust in Science Talk

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Towards the automated discovery of useful solubility applications

2011-03-27T21:57:00.000-04:00

Last week, I came across (via David Bradley) a paper by an MIT group regarding the desalination of water using a very clever application of solubility behavior:

Anurag Bajpayee, Tengfei Luo, Andrew Muto and Gang Chen, Energy Environ. Sci., 2011 Very low temperature membrane-free desalination by directional solvent extraction (article, summary)

The technique simply involves the heating of saltwater with molten decanoic acid to 40-80 C. Some water dissolves into the decanoic acid, leaving the salt behind. The layers are then separated and, upon cooling to 34C, sufficiently pure water separates out. Any traces of decanoic acid are inconsequential since this compound is already present in many foods at higher levels.

From a technological standpoint, I can't think of a reason why this solution could not have been discovered and implemented 100 years ago. It makes you wonder how many other elegant solutions to real problems could be uncovered by connecting the right pieces together.

To me, this is where the efforts of Open Science and the automation of the scientific process will pay off first. For this to happen on a global level, two key requirements must be met:

1) Information must be freely available, optimally as a web service (measurements if possible - otherwise a predicted value, preferably from an Open Model)
2) There has to be a significantly automated way of identifying what is important enough to be solved.

Since we have been working on fulfilling the first requirement for solubility data, I first looked at our available services to see if there was anything there that could have pointed towards this solution.

Although we have a measured (0.0004 M) and predicted (0.001 M) room temperature solubility of decanoic acid in water, our best prediction service can't do the opposite: the solubility of water in decanoic acid. For that we would need the Abraham descriptors for decanoic acid as a solvent and those are not yet available as far as I'm aware.

Also, we use a model to predict solubility at different temperatures - but it assumes that the solute is miscible with the solvent at its melting point. This is probably a reasonable assumption for the most part but it fails when the solute and the solvent are too radically dissimilar (e.g. water/hydrophobic organic compounds). In this particular application, decanoic acid melts at 31C and the process occurs in the 34-80 C range.

But even if we had the necessary models (and corresponding web services) for the decanoic acid/water/NaCl system, could it have been flagged in an automated way as being potentially "useful" or even "interesting"?

For utility assessment, humans are still the best source. Luckily, they often record this information tagged with common phrases in the introductory paragraphs of scientific documents. (In fact, this is the origin of the UsefulChem project). For example, if we search for "there is a pressing need for" AND solubility in a Google search, most of the results provide reasonable answers to the question of what a useful application of solubility might be. I have summarized the initial results in this sheet.

The first result is:
"there is a pressing need for new materials for efficient CO2 separation" from a Macromolecules article in 2005. The general problem needing solving would correspond to "global warming/CO2 sequestration" and the modeling challenge would be "gas solubility".

Analyzing the first 9 results in this way gives us the following problem types:

global warming/CO2 sequestration
fire control
global warming/refrigeration fluid
AIDS prevention
Iron absorption in developing countries
agriculture/making phosphate from rock bioavailable
water treatment/flocculation
natural gas purification/environmental
waste water treatment

and the following modeling challenges:

gas solubility
polymer solubility
hydrofluoroether solubility
solubility of drug in gels
inorganics
inorganics/pH dependence of solubility
polymer solubility/flocculation/colloidal dispersions
gas solubility
inorganics

These preliminary results are instructive. The problem types are broad and varied - and I think they will be helpful for keeping in mind as we continue to work on solubility. The modeling challenges can be compared directly with our existing services - and none of them overlap at this time! All of these involve either gasses, polymers, gels, salts, inorganics or colloids while our services are strictly for small, non-ionic organic compounds in liquid solvents.

Part of the reason for our focus on these types of compounds relates to our ulterior objective of assessing and synthesizing drug-like compounds. But a more important consideration is what type of information is available and what can be processed related to cheminformatics. Currently most cheminformatics tools deal only with organic chemicals, with essential sources such as ChemSpider and the CDK providing measurements, models, descriptors, etc.

Even though some inorganic compounds are on ChemSpider, most of the properties are unavailable. Consider the example of sodium chloride:

This doesn't mean that the situation is hopeless but it does make the challenge much more difficult. Solubility measurements and models for inorganic salts do exist (for example see Abdel-Halim et al.) but they are much more fragmented.

With the feedback we obtain from this search phrase approach - and hopefully help from experts in the chemistry community - we can piece together a federated service to provide reasonable estimates for most types of solubility behavior.

I think that this desalination solution will prove to be a good test for automated (or at least semi-automated) scientific discovery in the realm of open solubility information. In order to pass the test, the phrase searching algorithm should eventually identify desalination as a "useful problem to solve" and should connect with the predicted behavior of water/NaCl/decanoic acid (or other similar compound).

Luckily we have Don Pellegrino on board. His expertise on automated scientific discovery should prove quite valuable for this approach.

Open modeling of melting point data

2011-03-22T16:31:00.000-04:00

The contribution of Alfa Aesar melting point data to our open collection has facilitated the validation of a significant amount of the entire dataset. However, this process of curation is never-ending. A good example is the discovery of an error in one of the sources for the melting point of warfarin. Following David Weinberger's post about our melting point explorer, his brother Andy noticed a problem and this enabled us to fix it.

In a way, creating an open environment to make it easy to find and report errors - as well as add new data - complicates scientific evaluation. In order to report a reproducible process and outcome, it is necessary to take a snapshot of the dataset. Choosing the exact composition of a dataset for a particular application is somewhat arbitrary. Aside from selecting a threshold for excluding measurements that deviate too much, compounds may be excluded based on their type.

For the sake of clarity, we archived the various datasets we created from multiple sources with brief descriptions of the filtering and merging at each step. From the perspective of an organic chemist, ONSMP013 is probably the most useful at this time. It contains averaged measurements for 12634 organic compounds and excludes salts, inorganics or organometallics. The original file provided by Alfa Aesar contained several of these excluded compounds and can be obtained from ONSMP000. It might be interesting at some point to create a collection of melting points for inorganics or salts. We would welcome contributions of collections of melting points with different filters.

One of the advantages of ONSMP013 is that it is possible to generate CDK descriptors for each entry (and these are included in the spreadsheet). By not using commercial software to generate descriptors, it enables fully transparent modeling - and extension of that modeling by anyone.

With this in mind, Andrew Lang has used ONSMP013 to generate a Random forest melting point model (MPM002). The most important descriptors turned out to be the number of hydrogen bond donors and the Topological Polar Surface Area (TPSA). The scatter plot below shows the correlation (R2 = 0.79) between the predicted and experimental values. (color represents TPSA and size relates to H-bond donors)

Andy has described in much more detail the rationale for selecting the Random forest approach over a linear model in MPM001. He has also compared the performance of CDK descriptors versus those from a commercial program for a small set of drug melting points in MPM003.

The Random forest model (MPM002) is also now available as a web service by entering the ChemSpiderID (CSID) of a compound in a URL. See this example for benzoic acid. If experimental results exist they will appear on top and a link to obtain the predicted melting point will appear underneath.

Note that the current web service for predicting melting points can be slow - it may take a minute to process.

Additional web services for melting point data will be listed on the ONS web services wiki.

Validating Melting Point Data from Alfa Aesar, EPI and MDPI

2011-03-04T20:03:00.000-05:00

I recently reported that Alfa Aesar publicly released their melting point dataset for us to use to take into account temperature in solubility measurements. Since then, Andrew Lang, Antony Williams and I have had the opportunity to look into the details of this and other open melting point datasets. (See here for links and definitions of each dataset)

An initial evaluation by Andy found that the Alfa Aesar collection yielded better correlations with selected molecular descriptors compared to the Karthikeyan dataset (originally from MDPI), an open collection of melting points used by several researchers to provide predictive melting point models. This suggested that the quality of the Alfa Aesar dataset might be higher.

Inspection of the Karthikeyan dataset did reveal some anomalies that may account for the poor correlations. First there were several duplicates - identical compounds with different melting points, sometimes radically different (up to 176 C). A total of 33 duplicates (66 measurements) were found with a difference in melting points greater than 10 C.(see ONSMP008 dataset) Here are some examples.

A second problem we ran into involved difficulty processing the SMILES in the Karthikeyan collection. Most of these involved SO2 groups. An attempt to view this SMILES string in ChemSketch ends up with two extra hydrogens on the sulfur.

[S+2]([O-])([O-])(OCC#N)c1ccc(C)cc1

Other SMILES strings render with 5 bonds on a carbon and ChemSketch draws these with a red X on the problematic atom. See for example this SMILES string:

O=C(OC=1=C2C=CC=CC2=NC=1c1ccccc1)C

Note that the sulfur compounds appear to render correctly on Daylight's Depict site:

In total 311 problematic SMILES from the Karthikeyan collection were removed (see ONSMP009).

With the accumulation of melting point sources, overlapping coverage is revealing likely incorrect values. For example, 5 measurements are reported for phenylacetic acid.

Four of the values cluster very close to 77 C and the other - from the Karthikeyan dataset - is clearly an outlier at 150 C.

In order to predict the temperature dependence for the solutes in our database, Andy collected the EPI experimental melting points, which are listed under the predicted properties tab in ChemSpider (ultimately from the EPA). (There are predicted EPI values there but we only used the ones marked exp).

This collection of 150 compounds was then listed in a spreadsheet (ONSMP010) and each entry was marked as having only an EPI value (44 compounds) or having at least one other measurement from another source (106 compounds). Out of those having at least one more value, 10 reported significant differences (> 5C) between the measurements. Upon investigation, many of these point strongly to the error lying with the EPI dataset. For example, the EPI melting point for phenyl salicylate is over 85 C higher than that reported by both Sigma-Aldrich and Alfa Aesar.

These preliminary results suggest that as much as 10% of the EPI experimental melting point dataset is significantly in error. Only a systematic analysis over time will reveal the full extent of the deficiencies.

So far the Alfa Aesar dataset has not produced many outliers, when other sources are available for comparison. However, even here, there are some surprising results. One of the most well studied organic compounds - ethanol - is listed with a melting point of -130 C by Alfa Aesar, clearly an outlier from the other values clustered around -114 C.

When downloading the Karthikeyan dataset from Cheminformatics.org, a Trust Level field indicates: "High - Original Author Data".

It would be nice if it were that simple. Unfortunately there are no shortcuts. There is no place for trust in science. The best we can do is to collect several measurements from truly independent sources and look for consensus over time. Where consensus is not obvious and information sources are exhausted, performing new measurements will be the only option left to progress.

The idea that a dataset has been validated - and can be trusted completely - simply because it is attached to a peer-reviewed paper is a dangerous one. This is perhaps the rationale used by projects such as Dryad, where datasets are not accepted unless they are associated with a peer-reviewed paper. Peer review was not designed to validate datasets - even if we wanted it to, reviewers don't typically have access to enough information to do so.

The usefulness of a measurement is related much more to the details in the raw data provided by following the chain of provenance (when available) than it is in where it is published. To be fair, in the case of melting point measurements, there really isn't that much additional experimental information to provide, except perhaps an NMR of the sample to prove that it was completely dry. In such a case, we have no choice but to use redundancy until a consensus number is finally reached.

ONS Solubility Challenge Book cited in a Langmuir nanotechnology paper

2011-02-26T15:32:00.000-05:00

An interesting application of the data from the Open Notebook Science Solubility Challenge has recently been reported in Langmuir: "Enhanced Ordering in Gold Nanoparticles Self-Assembly through Excess Free Ligands" by Cindy Y. Lau, Huigao Duan, Fuke Wang, Chao Bin He, Hong Yee Low and Joel K. W. Yang (Feb 24, 2011).

The context is as follows, and the reference is to Edition 3 of the ONS Solubility Challenge Book.

Although to our best knowledge there lacks literature value of OA solubility in the two solvents, the 10-fold better solubility of 1-otadecylamine (sic), the saturated version of oleylamine, in toluene than hexane is in line with our hypothesis.(33) This increased solubility caused the OA molecules that were originally attached to the AuNPs to gradually detach from the AuNPs, which is supported by our observations in poor AuNP stability and surface-pressure isotherms.

This is a nice application of solubility to understand and control the behavior of gold nanoparticles. It is in line with some of the applications I discussed at a recent Nanoinformatics conference, where I think there is a place for the interlinking of information between solubility and nanotechnology databases.

I have to admit that it is somewhat ironic to see this citation in Langmuir, given the controversy about a year ago (post and FF discussion) regarding the citation of non-traditional literature.

Alfa Aesar melting point data now openly available

2011-02-21T21:01:00.000-05:00

A few weeks ago, John Shirley - Global Marketing Manager at Alfa Aesar - contacted me to discuss the Chemical Information Validation results I posted from my 2010 Chemical Information Retrieval class. Our research showed that Alfa Aesar was the second most common source of chemical property information from the class assignment.
We explored some possible ways that we could collaborate. With our recent report of the use of melting point measurements to predict temperature solubility curves, the Alfa Aesar melting point data collection could prove immensely useful for our Open Notebook Science solubility project.

However, since we are committed to working transparently, the only way we could accept the dataset is if it were shared as Open Data. I am extremely pleased to report that Alfa Aesar has agreed to this requirement and we hope that this gesture will encourage other chemical companies to follow suit.

The initial file provided by Alfa Aesar did not store melting points in a database ready format - it included ranges, non-numeric characters and entries reporting decomposition or sublimation. One of benefits we could provide back to the company was cleaning up the melting point field to pure numerical values ready for sorting and other database processing. This processed collection contains 12986 entries. Note that these entries are not necessarily different chemical compositions since they refer to specific catalog entries with different purities or packaging.

For our purposes of prioritizing organic chemicals for solubility modeling and applications we curated this initial dataset by collapsing redundant chemical compositions and excluded inorganics (including organometallics) and salts. We did retain organosilicon, organophosphorus and organoboron compounds. Because the primary key for all of our projects depend on ChemSpiderIDs, all compounds were assigned CSIDs by deposition in the ChemSpider database if necessary. SMILES were also provided for each entry, as well as a corresponding link to the Alfa Aesar catalog page. This curated collection contains 8739 entries.

For completeness, we thought it would be useful to merge the Alfa Aesar curated dataset with other collections for convenient federated searches. We thus added the Karthikeyan melting point dataset, which has been used in several cases to model melting point predictions. This dataset was downloaded from Cheminformatics.org. Although we were able to use most of the structures in that collection, a few hundred were left out because of some difficulty in resolving some of the SMILES, perhaps related to the differences in algorithms used by OpenBabel and OpenEye. Hopefully this issue will be resolved in a simple way and the whole dataset can be incorporated in the near future. This final curated collection contains 4084 entries.

Similarly the smaller Bergstrom dataset was included after processing the original file to a curated collection of 277 drug molecules.

Finally, the melting point entries from the ChemInfo Validation sheet itself, generated by student contributions, is added to amount to a collection of currently 13,436 Open Data melting point values. We believe that this is currently the largest such collection and that it should facilitate the development of completely transparent and free models for the prediction of melting points. As we have argued recently, improved access to measured or predicted melting points is critical to the prediction of the temperature dependence of solubility.

In addition to providing the melting point data in tabular format, Andrew Lang has created a convenient web based tool to explore the combined dataset. A drop down menu at the top allows quick access to a specific compound and reports the average melting point as well as a link to the information source. In the case of an Alfa Aesar source, a link to the catalog is provided, where the compound can be conveniently ordered if desired.
In another type of search, a SMARTS string can be entered with an optional range limit for the melting points. In the following example 14 hits are obtained for benzoic acid derivatives with melting points between 0C and 25C. Clicking on an image will reveal its source. (BTW even if you don't know how to perform sophisticated SMARTS queries, simply looking up the SMILES for a substructure on ChemSpider or ChemSketch will likely be sufficient for most types of queries).

Preliminary tests on a Droid smartphone indicate that these search capabilities work quite well.

Finally, I would like to thank Antony Williams, Andrew Lang and the people at Alfa Aesar (now added as an official sponsor) who contributed many hours to collecting, curating and coding for the final product we are presenting here. We hope that this will be of value to the researchers in the cheminformatics community for a variety of open projects where melting points play a role.

Predicting temperature-dependent solubility for solvent selection

2011-02-13T19:56:00.002-05:00

During the summer of 2010 I reported on the Solvent Selector web service that Andrew Lang and I constructed. The idea was to flag potential solvents with a high solubility for the reactants and a low solubility for the product, so that the work-up would require a simple filtration.

If available, the Solvent Selector service uses measured solubility values. If not available, it attempts to predict the room temperature solubility using one of two models based on Abraham descriptors.

We now have modified the Solvent Selector so that it takes into account temperature. Andrew has inserted a thermometer icon next to each solvent in the report. When clicked, a plot is displayed over the entire range of temperatures where the solvent is a liquid. Curves for each starting material and the product are provided - and hovering over a data point provides numerical values.

There are several ways this resource could be used by chemists.

For reactions where some starting materials are not soluble enough at room temperature, the reaction could be carried out at a higher temperature. A higher temperature might also be desirable simply to speed up the reaction. Being able to predict the solubility of the product at that higher temperature would allow the course of the reaction to be monitored by the appearance of a precipitate.

For reactions where the solubility of the product is too high at room temperature, the curves could be used to estimate how low one could cool the reaction mixture without any chance that one of the starting materials would precipitate out. For example, consider the following Ugi reaction.

An optimization study was performed and found that methanol and ethanol provided much better yields than THF.(see JoVE article) This makes sense from a solubility standpoint, where the Ugi product room temperature solubility is less than 0.05 M for methanol and ethanol but is 0.26 M for THF Solvent Selector results)

However, by clicking on the thermometer icon for the THF entry, one gets the following temperature curves for solubility.

By hovering over the curve for the Ugi product we find that the predicted solubility in THF at -78C (conveniently a dry ice in acetone bath) is 0.01M, while that for the starting material boc-glycine is 0.65 M, well above the 0.5 M concentration at the start of the reaction. This means that even if the reaction did not take place to a significant extent, we would not expect the starting material to precipitate at -78C. Any precipitate should be the pure product.

Of course another obvious application is for solvent selection for re-crystallization.

How it works

For some time now we have been collecting literature on the temperature dependence of solubility in various systems (live Mendeley collection here - be patient it might take a minute to display). Although equations vary depending on the specific approach there seem to be the following commonalities:

An assumption is made that miscibility is reached at the melting point of the solute.
The log of the solubility is linearly proportional to the inverse of the temperature in Kelvin.

I have looked into a few examples that we have of solubility over a temperature range and the above do seem to hold. This means that with only a room temperature solubility and a melting point for a given solute, the solubility at any temperature can be interpolated or extrapolated. (The concentration at the miscibility point is calculated from the predicted density of the solute divided by its molecular weight).

Although there are situations where two liquids are not miscible because of extreme dissimilarity (e.g. methanol and hexane), for the most part our experience shows that the first assumption is valid. Also, we are not correcting for changes in density for the solute or solvent at different temperatures. Nevertheless, when only a single solubility measurement in a given solvent and a melting point are known, this simple model may prove to be of use as a rough guide for reaction design or re-crystallization. We'll report on its practicality over time as we put it to use.

The Spectral Game with ChemDoodle

2011-02-10T11:56:00.000-05:00

In the summer of 2009, we published an article on the Spectral Game. This game is based on spectra uploaded as Open Data (in JCAMP-DX format) on ChemSpider (currently about 2000 H NMRs and a few C NMRs, IRs and NIRs). Students get points by clicking on the molecule associated with the spectrum on display.

Although this has proved to be a useful tool to teach spectroscopy (especially H NMR), there have been some limitations, which are related to the use of Java (JSpecView) to provide an interactive display of the spectra.

1) Spectra do not display properly on Macs - there are problems with the "right-click" options in JSpecView. It took me a really long time to understand why some of my friends were really unimpressed by JSpecView. When I recognized that they were all Mac users I took a look and it became clear.

2) Spectra do not display at all on smartphones because of the Java components

I am very happy to report that these issues have been overcome (for the most part) using ChemDoodle. Through a collaborative effort between Kevin Theisen, Andrew Lang, Antony Williams and myself, we now have a non-Java based version of the Spectral Game at SpectralGame.com.

The game plays well on Mac, iPhone and iPad. However I have seen it fail on 2 Androids so there are still a few kinks to work out. Luckily I happen to be teaching NMR right now in my organic chemistry course so my students will be testing out the ChemDoodle version extensively.

There are some really nice additional features as well. My favorite is the auto-scaling of the integration line when zooming in. In the JSpecView version, integration is problematic because, when zooming into high field peaks, the start of the integration line does not reset to zero and this requires several iterations of changing the integration offset to get a usable measurement.

Another advantage in the ChemDoodle design is the simplicity of the interface. There are no right-click options: everything available is clearly labeled at all times (toggle integration, reset spectrum and view header information). This makes the game easier to learn and play.

I would especially like to thank Kevin Theisen for being so responsive on the ChemDoodle end. I was skeptical that we would have a playable game for this term but he addressed all of our major issues very quickly.

Science Online 2011 Thoughts

2011-01-17T15:59:00.001-05:00

On January 15, 2011 I co-moderated a Science Online 2011 session on Open Notebook Science with Antony Williams and Carl Boettiger. The projector failed so we did our best to introduce the topic without relying on visual aids. My main objective was to demonstrate that it is not necessary for researchers (or their machines) to interface with the actual lab notebook to benefit from the information generated from the work. By introducing simple and rapid abstraction steps, both solubility and reaction information can be converted to web services for a variety of uses. As long as a link to the original lab notebook page (including the raw data) is attached, no information is lost and details can be investigated on demand.

One of the most powerful tools to use in this context is the tracking of chemical entities as ChemSpider IDs. This enables direct access to many other web services which Andrew Lang and I have leveraged to generate our own services. Tony spoke a bit more about this in his part and outlined some of the benefits and frustrations with crowdsourcing. Carl spoke eloquently about his experiences with Open Notebook Science as a graduate student for computational projects. The slides from all of us are provided below.

Science Online 2011 ONS session

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The overall tone of the discussion during our session was quite positive and productive. This was the case with all of the other sessions I attended, as it has been in prior years. The Science Online conference has evolved to attract a large proportion of people advocating Open Science. The presenters and the audience feel that they are among friends and the result is usually a free and easy exchange of ideas. Not all conferences and symposia relating to the online aspects of science share this. I have seen many examples where the "online science" theme is overrun by Closed Science proponents, for example commercial databases or Electronic Laboratory Notebook (ELN) vendors. Hopefully this conference will retain its Open Science focus in the future.

Kaitlin Thaney proved to be a very effective moderator during her session on "The Digital Toolbox: What's Needed?" and she stirred up some insightful discussion. I also enjoyed Steve Koch's session (co-moderated with Kiyomi Deards and Molly Keener) on "Data Discoverability: Institutional Support Strategies". Steve shared a particularly compelling example of the collaborative benefits of Open Notebook Science, where a computational research group came across images and videos from one of his group's notebooks and incorporated these in their paper - with all due credit acknowledged.

I very much appreciated the opportunity to catch up with old friends and some new. I had never met Carl Boettiger in person before and we had some very interesting discussions about Open Science and Open Education. It was good to meet Mark Hahnel from FigShare and explore possible paths for data sharing. I had some nice chats with Antony Williams, Steve Koch, Steven Bachrach, Heather Piwowar and Ana Nelson.

The Saturday evening banquet proved to be surprisingly entertaining. Despite the sedate title of her talk, "Out on a Limb: Challenges of Training Scientists to Communicate", Meg Lowman pounded the audience with a hilarious performance. Science comedian Brian Malow kicked this up a notch with some very clever material. Later on, using a brilliant comedic judo technique, he repeated some choice derisive comments he received from his performances on YouTube. I hope he comes back next year!

Talk on Open Education in Chemistry at the University of the Sciences

2011-01-12T18:41:00.000-05:00

I presented on "Open Education in Chemistry Research and Classroom" at the University of the Sciences on January 11, 2011. The talk covered screencasting, wikis, Open Notebook Science, games and smartphones.

This was also an opportunity to present some new work Andrew Lang and I did on Chemical Information Validation, resulting from the students in my Chemical Information Retrieval class during the Fall 2010 term. I also highlighted Don Pellegrino's recent social/chemical network visualization project.

Philadelphia U Sciences 2011

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Chemical Information Validation Results from Fall 2010

2011-01-05T13:56:00.000-05:00

As I mentioned earlier, one of the outcomes from my Fall 2010 Chemical Information Retrieval class involved the collection of chemical property information from different sources in a database format. Now that the course is over, this has resulted in 567 measurements for 24 compounds (including one compound EGCG from the previous term). I have curated the dataset to ensure that the original numbers, conversions to common units, categorizations, etc. are correct. Links to the information source or to a screenshot of the source are available for each entry - so if I missed something, anyone can unambiguously verify it for correction.

The dataset is available from a Google Spreadsheet. Andrew Lang has also created a web based interface: the ChemInfo Validation Explorer. By simply specifying the compound of interest and the property using drop-down menus the list of measurements from the relevant sources is provided with values outside of one standard deviation marked in orange. Links to the information source, or an image in cases where the information source cannot be directly linked, are provided in the results. Here is an example for the boiling point of benzene.

The visualization and analysis of the data was greatly facilitated by the use of Tableau Public. After downloading the free program anyone can easily re-create the queries in this post by first downloading the dataset as an Excel document then importing into Tableau Public. Interactive charts can then be freely hosted on the TP server and embedded as I have done in this post below.

The students were shown how to search both commercial and free information sources and were given complete freedom for which compounds and chemical properties to target. The results can be analyzed from the perspective of a reasonable sampling of the current state of chemical information available to the average chemist. The 5 most frequently obtained properties were melting point, density, boiling point, flash point and refractive index.

The information sources were categorized and are reported below by frequency. Chemical vendor sites were by far the most frequently used information source.

It is important to note that the information source does not represent the method by which the measurements were found. The source is simply the end of the chain of provenance: the document that provides no specific reference for the reported measurement. For example, even though ChemSpider was frequently used as a search engine, it would not be listed as an information source when it provided links to other sources (mainly MSDS sheets) for properties. ChemSpider was treated as a source for some predicted properties.

The chemical vendor Sigma-Aldrich was the most frequently used information source, followed by Alfa Aesar. Wolfram Alpha - categorized as a "free database" was third. Oxford University follows closely behind as fourth and is categorized as an "academic website", hosting MSDS sheets. Many universities host MSDS sheets but the Oxford web site seems to turn up most frequently from chemical property queries on search engines.

The fifth most frequent information source was Wikipedia, reflecting the fact that specific references are usually not provided there for chemical properties. Like ChemSpider, Wikipedia was categorized as a "crowdsourced database".

Flagging Outliers

One of the advantages of this type of collection is that it is much easier to identify outliers. In the case of non-aqueous solubility data, we were able to create an outlier bot to automatically flag potentially problematic results. Since different properties may have very different typical variabilities, outliers are most easily discovered by comparisons within the same property.

For example consider the following plot showing the standard deviation to mean ratio for melting point measurements.

This reveals that the average melting point for EGCG is suspect. At this point, an easy way to inspect the results is to use the Validation Explorer and look at the individual measurements.

By clicking on the images we can verify that the numbers have been correctly copied from the primary sources. In this case we can also ascertain that the sources - a peer reviewed paper and the Merck Index - are considered by most chemists to be generally reliable. There is no compelling reason at this point to weigh one result over the other and one has to be careful when using the average value for any practical application. (Note that all temperature data is recorded as Kelvin. A zero-based scale is necessary to ensure that the standard deviation to mean ratio is meaningful.)

The next flagging hit in this collection is the melting point of cyclohexanone. In this case 5 results are returned and the Validation Explorer highlights the Alfa Aesar value as being more than one standard deviation from the average.

However, one has to be careful when assessing this and assuming that the Alfa Aesar value is most likely to being the odd value out. Notice that 3 of the values - Sigma-Aldrich, Acros and Wolfram Alpha are identical. The most likely explanation for this is that all three used the same information source and should thus be counted as a single measurement.

We can test this hypothesis by looking for cases where Sigma-Aldrich, Acros and Wolfram Alpha don't share identical values. As shown below, for melting point measurements, there is no case where the values don't match.

The same is true for boiling points:

However, in the case of flash points it is clear that the three are not using a common data source.

Using the data we collected - and will continue to collect - we could start to identify which data sources are likely using the same ultimate sources and avoid over-counting measurements. This would save time in searching since one would know which sources to check for a particular property while avoiding duplication. This information is extremely difficult to obtain using other approaches.

The same type of outlier analysis can be performed for all the properties collected in this study.

I believe that there is much more useful analysis to be done on this dataset, especially for chemistry librarians. When this class is run next year, more data will be added. In the meantime, contributions from other sources would be welcome.

Visualizing Social Networks in Open Notebooks

2010-12-20T12:56:00.000-05:00

Increasing the role of automation in the scientific process has long been a fundamental objective of Open Notebook Science. The automatic discovery of new connections in open scientific work is potentially a very important contribution to this end.

Visualizing social networks within and between Open Notebooks is certainly a good first step. Luckily, our Reaction Attempts project has already abstracted the key elements of organic chemical reactions within a collection of Open Notebooks. This means that creating connection maps between people and chemicals can be attempted with reliable and semantically unambiguous database sources.

The Reaction Attempts database records the identity of reactants and products as ChemSpiderIDs for each reaction within a collection of notebooks. Also the name of the researcher, the solvent, the yield (when available) and a few more key identifiers are recorded.

We are very fortunate that Don Pellegrino, an IST student at Drexel, has selected the analysis of networks within Open Notebooks as part of his Ph.D. work. He has started to report his progress on our wiki and is eager to receive any feedback as the work progresses (his FriendFeed account is donpellegrino).

Don's first report is available here. He is using the Open Source software Gephi for visualization and has provided all of the data and code on the associated wiki page. (also see Tony Hirst's description of mapping ONS work which provided some very useful insights) Don has provided a detailed report of his findings but I think the most important can be seen in the global plot below.
This represents a map connecting people through chemicals. The large top right structure represents the connections within the UsefulChem project and the main circle represents the activity of graduate student Khalid Mirza who was the most active on this project. The crescent structure to the right of the circle represents other students - mainly undergraduates - who worked with the same chemicals as Khalid.

At the top left there are 3 isolated small networks, representing completely separate projects: the sodium hydride (NaH) oxidation study, Dustin Sprouse and Sebastian Petrik. I'll be posting about Sebastian's work in a future post.

Near the bottom middle there is another small network connected to the main group by a single link mediated by 2,2-dimethoxyethylamine.

This represents the overlap between Open Notebooks (Wolfle from Todd group and Mirza from Bradley group) that I mentioned previously.

I think that automatically discovering such connections as they occur could be a really useful outcome of this network analysis work. For example, the researchers could be alerted by email that a new potentially interesting overlap between their projects now exists. This could accelerate new collaborations.

A key challenge in Don's work is to figure out the right questions so that the results will be genuinely useful and novel to the researchers involved and the research community. I'm optimistic that he will succeed. As a separate outcome, just learning how researchers collaborate and record their work over time is bound to be interesting.

For a description of Don's planned work over the next several months take a look at his full Thesis Proposal: "Proposal of a System and Methods for Integrating Literature and Data".

Mirza PhD defense on the Ugi reaction for anti-malarial screening

2010-12-13T09:16:00.000-05:00

My student Khalid Baig Mirza defended his Ph.D. thesis at Drexel University on December 6, 2010. It was nice to see all the pieces come together - even though there are still a few intriguing puzzles left to be fully resolved. Like most research projects, every answer generates several interesting additional questions and Khalid did a good job of showing what these key issues are for his work.

In his presentation, Khalid first discusses Open Notebook Science and his contribution to the sodium hydride oxidation controversy. Then he describes the UsefulChem project, involving the use of the Ugi reaction as an approach to synthesizing new anti-malarial agents, including a few unexpected side reactions and challenges. Finally he presents an overview of the ONS Solubility Challenge and its application to organic synthesis.

Mirza PhD defense on the Ugi reaction for anti-malarial screening

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Dana Vanderwall on Cheminformatics at Drexel

2010-12-05T15:29:00.000-05:00

Dana Vanderwall, Associate Director of Cheminformatics at Bristol-Myers Squibb, presented for my last Chemical Information Retrieval class on December 2, 2010.

The first part covered "Cheminformatics & The evolving relationship between data in the public domain & pharma" and included a general discussion of modern drug discovery and the details of a malaria dataset recently released from the pharmaceutical industry to the public.

Vanderwall cheminformatics Drexel Part 1

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The second part described a project based on "Molecular Clinical Safety Intelligence", where tracking side effects from approved drugs can help in the design of new drugs.

Dana Vanderwall cheminformatics Drexel Part 2

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It was a very nice way to close out the course, showing very practical applications of the concepts we covered over the term. The recording is available below.

Nanoinformatics 2010 Conference Report

2010-11-04T12:03:00.000-04:00

On November 3, 2010 I presented on "The implications of Open Notebook Science and other new forms of scientific communication for Nanoinformatics" at the Nanoinformatics 2010 conference.

The presentation first covers the use of the laboratory knowledge management system SMIRP for nanotechnology applications during the period of 1999-2001 at Drexel University. The exporting of single experiments from SMIRP and publication to the Chemistry Preprint Archive is then described followed by the evolution to Open Notebook Science in 2005. Abstraction of semantic structure from ONS projects in the areas of drug discovery and solubility is then detailed as an efficient mechanism to provide web services and machine readable data feeds.

This was a terrific opportunity to tie together my current ONS projects with my work in nanotechnology about 10 years ago, when the focus was to capture laboratory information in a structured format so that autonomous agent could begin to replace human workflows. I found it really interesting that the most active workflows back then were related to processing reference information. It took a team of students to find, photocopy and scan many of our key papers, with all the problems that come with training and managing new students. Today, obtaining relevant papers and extracting metadata is not so much of a challenge with tools like Mendeley. I ended the talk with a mention of our use of Mendeley tags to share dynamic links of article collections.

Another important development over the course of the past decade is the availability of free and hosted tools to easily communicate research. This includes wikis, blogs, Nature Precedings, institutional repositories, Google Spreadsheets and many others. It also includes some failed attempts like the Chemistry Preprint Archive.

I didn't anticipate in the late 90s just how crucial openness would prove to be for the evolution of the automation of the scientific process. It isn't my impression that there is currently a consensus on this point. Obviously it is possible to leverage automation in very clever ways for private use. But I think that exponential impact requires very low barriers to contribution (human or not) that can only be achieved with openness and transparency.

I have been very impressed with the ideas and projects discussed at this conference. Open sharing of nanotechnology data and integration of resources are clearly high priority items for many in this community.

As we have shown with our Reaction Attempts and ONS Solubility projects, abstracting meaningful semantic structure is necessarily field specific. One of the exciting opportunities to result from this meeting is finding ways to interconnect our solubility dataset with the nanotechnology community resources. I have met with a few people who would like to collaborate on this and I will be sure to report on our progress.

Nanoinformatics 2010 SMIRP-ONS Talk

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Sozit Kurtu is Nov10 RSC ONS Award Winner

2010-11-04T09:15:00.000-04:00

Sozit Kurtu, a chemistry student working under the supervision of Jean-Claude Bradley at Drexel University, is the November 2010 Royal Society of Chemistry Open Notebook Science Challenge Award winner. She wins a cash prize from the RSC.

Sozit has performed important work in determining the accuracy of a density method of determining solubility and has explored the temperature dependence of the solubility of low melting point solutes in hexane. See her experiments here:
http://onschallenge.wikispaces.com/list+of+experiments

Four more RSC ONS Awards will be made during 2010-11. Submissions from students in the US and the UK are still welcome.
For more information see:
http://onschallenge.wikispaces.com
http://onschallenge.wikispaces.com/RSCAwards2010

Elizabeth Brown's guest lecture for ChemInfo Retrieval

2010-10-26T11:02:00.000-04:00

Elizabeth Brown from the Binghamton University Libraries presented on "Web 0.0/1.0/2.0/3.0 and Chemical Information" on October 21, 2010 as a guest lecturer for my fifth class on Chemical Information Retrieval this term.

Chemical information retrieval class 10 21 2010

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Beth made an interesting analogy with art to illustrate the differences between these communication platforms. What stuck me during her presentation was the similarity between the current state of the semantic web (Web3.0) and the state of computerized searching in chemistry when I was a graduate student in the early 90s. I had the chance to do just one substructure search at the time and it had to be done through an expert librarian. The search had to be carefully planned because of the expense.

From what I recall, the perception at the time was that computerized searching was impractical and perhaps even unnecessary. After all "the way" to search for chemical information was to spend a weekend in the library systematically going through Chemical Abstracts books. This had "worked" for long time for the chemistry community and doing things differently was considered superfluous and even wasteful.

Today, "the way" to search for chemical information is to use expensive databases to perform a targeted search and extract the information from mainly toll access peer reviewed journals. If you are off the academic grid you have to rely on free information on the web which is rarely associated with a chain of provenance. As my students will attest, even with access to the best tools, it still takes a lot of time to find the information and compare it for consistency.

The unfamiliarity with computerized searching in the early 90s is now the common attitude towards the semantic web. This is understandable because its availability is limited and people don't understand what to do with the tools that are available. Web services that we provide (derived from other services from ChemSpider and other sources) are usable by anyone who can open up a Google Spreadsheet and copy and paste a URL but it will take time for people to understand how to incorporate these into their workflows.

I think that in 10 years the semantic web will simply be part of the infrastructure. Currently, when you type a word in a browser or word processing software the system knows enough to alert you to possible misspelling by underlining a word. Similarly, in the near future, typing or specifying in some way a chemical compound will automatically pull in all relevant measured or calculated properties and provide suggestions in the context of a chemical reaction under consideration. Access to information will be free and unencumbered and the chain of provenance will be clear.

Instead of taking hours to find and process property data for single compounds, I think students in the future will be handling large libraries of compounds, looking for the best synthetic targets for their applications.

Dynamic links to private tagged Mendeley collections

2010-10-15T16:20:00.000-04:00

My close collaborators and I have been using Mendeley as a convenient way to share PDFs of journal articles. Not all of us have access to the same libraries so links are not enough - we need the full documents. We also use Dropbox as a redundancy but Mendeley allows tagging and recording notes, which is very handy for everyone in the group.

Now that Mendeley is providing an API, Andrew Lang has written code that significantly leverages the information in our private ONS collection. We can now create public links that return the most updated results for specific tags, including multiple tags (which I don't think you can do on Mendeley). For example the following link returns all articles in the ONS collection tagged with "science2.0" and "chemistry":

http://showme.physics.drexel.edu/onsc/mendeley/?tags=science2.0,chemistry

The results include available information from Mendeley, including the title, authors, journal citation, doi, url, tags and the abstract. Because this information is public the PDFs can't be provided but the hyperlinks make it as convenient as possible.

At the end of the report the full list of all available tags for the ONS collection is provided. A more refined or different search can be done immediately simply by checking boxes and hitting the submit button.Because the tags are controlled by the users of the private collection, these links can be useful when discussing an ongoing project and referring to a very specific topic. For example, we have been collecting examples of articles where a Ugi reaction is carried out and the product precipitates. This link provides an updated report on that very narrow topic:

http://showme.physics.drexel.edu/onsc/mendeley/?tags=Ugi+precipitate

There are still 2 major limitations to this service:

1) The search is very slow (can take a minute or two) because there is no way currently to use the Mendeley API to selectively return results based on tags. Every search requires initially returning all results for the collection (currently a few hundred).

2) Notes are currently not returned. If the API is updated to include these the usefulness would increase dramatically. For example in the results for the above query I took notes of the conditions involved in the Ugi precipitate for each paper. With the current format, one has to read each paper to find the relevant information.

Progress on our Mendeley related services will be posted on the ONSwebservices wiki.

Drexel Chemistry Mini-Symposium on Bradley Lab

2010-10-07T12:47:00.001-04:00

Every year the chemistry department at Drexel gives faculty the opportunity to present their research to incoming students in 10 minutes slots. On September 30, 2010 I presented on "Open Notebook Science for Malaria Drug Discovery and Solubility Modeling". I think such a short format is good for keeping student attention. Recording it also provides a handy link to use for other purposes. Most people just don't have time for 30-60 minute presentations.

Bradley Drexel MiniSymp 2010

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The Meaning of Data panel at a class on the Rhetoric of Science

2010-10-07T11:22:00.001-04:00

Update: Lawrence Souder provided the audio for the presentation and panel questions.

On October 6, 2010 I had the pleasure to participate in a panel discussion at Lawrence Souder's class on the Rhetoric of Science. I gave a brief presentation on "The Meaning of Data" to kick off the discussion. I provided a scientist's perspective of data - with the basic idea that at best data are evidence. Data cannot be treated as irrefutable facts since there is always some uncertainty and many assumptions must be made in their interpretation. I also argued that, although uncertainty cannot be eliminated completely, transparency goes a long way to reducing it.

The students in the class did not have a science background and I think it was informative for them to explore a different perspective from their other exposure to science through popular media or even textbooks. The term "fact" is thrown around a lot in these information sources to simplify but it doesn't reflect how scientists think about data. We discussed how unbelievably wrong the scientific details in movies and TV shows such as NCIS and House can be. Nevertheless, one student pointed out that these shows can be effective in attracting people to science - as long as it was understood that the details were probably incorrect.

Because of market forces we recognized that science portrayed in the popular media would have a strong tendency to be exaggerated or oversimplified resulting in the phenomenon of "hype". A related effect can distort the way scientists communicate with each other, where there is a perception that exposing ambivalence in the form of seemingly contradictory data may not be in the researcher's best interest. This is a strong deterrent to the general adoption of transparency.

We explored the possible evolution of scientific communication as new tools such as blogs and wikis become increasingly used by scientists. Of course the issue of claims of priority was brought up and I discussed how this issue was handled in my own research work.

We debated whether these new forms of communication would alter the language used in scientific communication - even in traditional journals. I think that with the advent of the semantic web many researchers will start to write in a way that is understandable to humans as well as machines, their new target audience. One student remarked that the new generation coming up is very used to texting and that type of succinct communication is certainly in line with machine readability.

The assigned reading for the class involved a study of the language used by the Nobel laureates who discovered the buckyball to describe their research:

"In Praise of Carbon, In Praise of Science - The Epideictic Rhetoric of the 1996 Nobel Lectures in Chemistry by Christian Casper". This paper demonstrates that both personality and the perceived target audience can dramatically affect the language and focus that scientists use to explain their research.

The Meaning of Data

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Cheminfo Retrieval Classes 1 and 2 in 2010

2010-10-06T13:21:00.000-04:00

My first Chemical Information Retrieval class for the Fall of 2010 took place on Sept 23, 2010. This is the second time that I've taught the class as sole instructor and it was certainly convenient to have last year's wiki to build upon. The assignments are the same so it was helpful to be able to give students access to what students did last year as examples.

The key message from my introductory lecture was that it can be really difficult to find usable chemical information and that there are no shortcuts like relying on a true trusted source - those don't exist. I showed a few examples of emerging models - Open Access, Open Notebook Science, Collaborative Competition (like pharma companies sharing some drug data openly) and other Open Science initiatives.

I also announced that we would be doing something new in the Science3.0 theme (the semantic web). One of the assignments involves collecting 5 values from the literature for each of 5 properties for a compound of the student's choice. In addition to adding these values on the wiki, we will collect them in a format that is friendly to machines: a ChemInfo Validation Google Spreadsheet. Andrew Lang has agreed to help with adapting our previous code for solubility to creating web services for this application. For example, we can have a service that reports the mean and standard deviation for a particular property and chemical. Another could produce statistics for a given data source or compare peer reviewed vs non peer reviewed sources, etc. Since it will be possible to to call these web services from within a Google Spreadsheet or Excel it should enable much more sophisticated analysis of the data related to the "validity" of chemical information as it exists today.

I didn't record the first lecture but I have the slides below:

Cheminfo Retrieval 2010 Class 1

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During the second lecture on September 30, 2010 I spent most of the time showing students how to use Beilstein Crossfire, SciFinder and ChemSpider to find values for chemical properties. The recording for the second lecture is available below:

Impact of Open Notebook Science Interview

2010-09-01T10:21:00.000-04:00

Richard Poynder's interview of me on Information Today is now available: "The Impact of Open Notebook Science". I appreciate the time he took to go into this much depth.

Open Notebook Science in Drug Discovery at Opal Event

2010-08-26T10:21:00.001-04:00

I presented on "Open Notebook Science in Drug Discovery" on August 24, 2010 at a panel on Industry and Academia part of the Opal Event "Drug Discovery: Easing the Bottleneck".

Open Notebook Science in Drug Discovery

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I only had about 15 minutes to present so I could not go into much detail but I did want to highlight the most recent work Andrew Lang and I (also with Peter Li from ChemTaverna) carried out involving solubility prediction and web services. Most of the attendees were from industry and I appropriately used the recent GSK malaria data sharing to introduce the talk. It is clear that there is a role for Open Science in drug discovery and I think that industry involvement will continue to increase in this area.

My co-panelist Rathindra Bose from Ohio University presented on his group's development of a novel cancer treatment compound based on platinum. He made the point that academic research complements that from industry by being able to explore more speculative hypotheses. The dominant hypothesis for the mechanism of action of platinum based drugs is binding with DNA. By exploring alternative scenarios, his group found an active platinum drug that does not bind with DNA.

During the preceding session on the Emergence of Biologics in Drug Discovery, Albert Giovanella from the University of Pennsylvania School of Medicine gave a particularly enlightening talk about comparing biologics with small molecule drugs. Although biological drugs tend to have less toxicity, the overall cost to bring them to market is still quite high and their cost to the consumer may be so high as to limit their impact. It looks like it will not be generally easy to translate new biomedical knowledge to a widespread impact on human health.

ChemTaverna Workflows of ONS Web Services now on MyExperiment

2010-08-23T16:48:00.000-04:00

I'm pleased to report that one of the collaborations initiated at the Berkeley Open Science conference last month is progressing very well.

Carole Goble introduced me to Peter Li who runs the ChemTaverna project. The idea was to use Taverna to construct workflows using the web services developed by Andrew Lang for our Open Notebook Science projects: UsefulChem and the ONS Solubility Challenge.

Peter quickly created several workflows to demonstrate what is possible. Here is a workflow that uses a Google Spreadsheet as input. SMILES for amines, carboxylic acids, aldehydes and isonitriles are entered in the appropriate columns. The workflow first creates a virtual library of Ugi products from all possible combinations of reactants. Then each product is submitted to a web service that predicts the solubility in methanol, the most common solvent for Ugi reactions.

The resulting spreadsheet can then be sorted by predicted solubility to recommend products that are more likely to precipitate from the reaction mixture. In this particular example Ugi products derived from boc-glycine are predicted to have a low solubility in methanol. The least soluble compound is predicted to have a solubility of only 0.07MIn this library, Ugi products derived from boc-methionine are predicted to be too soluble to precipitate. For example this Ugi product has a predicted solubility of 3.7 M.
(note: ChemSpider has a tendency to draw the minor tautomer for some amides and carbamates)

There are a few issues to take into consideration in order to use this particular workflow:

1) This will only work on Taverna Workbench 2.1.2 with these plug-ins installed. At one point it will be made to work on Taverna Workbench 2.2 and uploaded onto MyExperiment. The workflow used here is currently available here.

2) The SMILES in the input Google Spreadsheet must be written in the format of the current example (aldehyde, amine and isonitrile groups on the left and carboxylic acid groups on the right)

3) All of the Ugi products in the virtual library must already exist in ChemSpider. Otherwise, the solubility predictions will fail because of missing descriptors as discussed previously.

Peter has uploaded simpler workflows onto MyExperiment that are compatible with the current version of Taverna Workbench (v2.2).

First, the generation of Ugi product libraries from reactant SMILES in a Google Spreadsheet is available here.

Another workflow handles the prediction of Abraham descriptors.
This workflow processes the prediction of solubility for a given solute and solvent.

The main rationale for incorporating web services derived from our Open Notebook Science projects into Taverna is leverage. MyExperiment already benefits from a vigorous community of developers in the bioinformatics arena. With the growth of the ChemTaverna initiative, the integration of cheminformatics and bioinformatics workflows should become seamless.

By making our solubility and chemical reaction web services available in formats that are convenient for others to use it increases the opportunities that our work will be actually useful. It also makes it easier for us to leverage the resources made available by others for our own applications in drug discovery and reaction design.

Essentially this means that we have extended the reach of the information cascade triggered by the recording of an experiment in a laboratory notebook and a very simple abstraction process to represent that experiment in a semantically addressable format.

ONS Challenge Solubility Data used in COSMO-RS Tutorial

2010-08-18T11:45:00.001-04:00

Update: Andreas Klamt pointed out to me that the best source of his software is the COSMOtherm package from COSMOlogic.

It is really interesting to see how our solubility data from the ONS Challenge are being used. In this example, our measurements for vanillin in various solvents are being compared to predicted values in a tutorial for the commercial software package COSMO-RS.

Green Solvent Metric on Solvent Predictor

2010-08-16T10:35:00.000-04:00

In the spirit of contributing to Peter Murray-Rust's initiative to collect Green Chemistry information, Andrew Lang and I have added a green solvent metric for 28 of the 72 solvents we include in our Solvent Selector service. The scale represents the combined contributions for Safety, Health and Environment (SHE) as calculated by ETH Zurich.

For example consider the following Ugi reaction solvent selection. Using the default thresholds, 6 solvents are proposed and 5 have SHE values. Assuming there are no additional selection factors, a chemist might start with ethyl acetate with a SHE value of 2.9 rather than acetonitrile with a value of 4.6.

Individual values of Safety, Health and Environment for each solvent are available from the ETH tool. We are just including the sum of the three out of convenience.

Note that the license for using the data from this tool requires citing this reference:

Koller, G., U. Fischer, and K. Hungerbühler, (2000). Assessing Safety, Health and Environmental Impact during Early Process Development. Industrial & Engineering Chemistry Research 39: 960-972.

The Reaction Attempts Solvent Selector

2010-08-06T13:09:00.000-04:00

The ONS Solubility Challenge and the Reaction Attempts project have now been integrated with code written by Andrew Lang to the point that recommendations for solvents are just a click away.

First use the Reaction Attempts Explorer either using the drop-down menus or substructure search as described previously. When a reaction of interest is identified just click on the the link for "Optimal Solvent Prediction".

The service will then provide a summary of solubility measurements and predictions, organized by the default criteria of minimum 0.3 M solubility of reactants, maximum 0.03 M solubility of the product and maximum solvent boiling point of 100 C. Liquid reactants (or reactants with melting points within 15 C of room temperature) are excluded since these generally have a high enough solubility in most solvents.

In the case of the Ugi reaction in this example, only the solubility of boc-glycine and the product are considered.

The results are color-coded. In this case 14 solvents are coded green, indicating that all criteria were met. The fifteenth solvent is coded yellow, indicating that one of the criteria was not met - in this case the boiling point of 205 C is outside of the limit of 100 C. High boiling point solvents are not optimal for quickly obtaining the product as a dry solid after filtering. This criterion can be changed in the input fields at the top of the page. It is also possible to change the number of times the product is washed there. This will only change the estimated yield, which is based on carrying out the reaction at the concentration of the least soluble reactant, up to 1 M.

Three columns are generated for the product and each reactant. The column on the right is the average of all measurements, as recorded in the SolubilitiesSum Spreadsheet. The middle column is a solubility prediction based on Abraham descriptors derived from experimental values, as described and used in the ONS Solubility Challenge book. The column on the left contains predictions from the Abraham001 model, which is based on calculated molecular descriptors only.

The numbers in bold represent the best solubility value available for each solvent. If a measurement is known, that will be the number used. If no measurement is available, the experimental Abraham descriptor model is used. If neither of these are available the predictions from the Abraham001 model are used by default.

From the list of solvents in the green section we find ethanol and acetonitrile. Both of these solvents were tried (as mixtures with methanol) in the optimization of this reaction (Bradley et al JoVE 2008) and provided good to intermediate results. THF was found to give low yields for this reaction and it scores at #51 in the yellow section, with a high solubility of the product accounting for the missed criterion.

One should keep in mind that this is just a tool to flag potentially interesting solvents. Common chemical sense needs to be used as well. For example, acetone and butanone are listed in the green section but these are incompatible with the Ugi reaction since they would compete with the aldehyde.

Note that the predictive models are way off in some cases. For example the Abraham001 model dramatically underestimates the solubilities of boc-glycine in the green section, while the measured Abraham descriptor model does much better for these cases. We will prioritize our next solubility measurements to try to improve the models - or at least understand what types of compounds are most likely to yield useful solubility estimates from these models.

In addition to being called from the Reaction Attempts Explorer, the Solvent Selector can be used for any compounds that have ChemSpider IDs. Simply separate the CSIDs with the pipe character:

http://showme.physics.drexel.edu/onsc/models/solventselector.php?csids=70660|21106059

After modifying the criteria and hitting update, the new criteria are conveniently represented in the URL in this format, making sharing a specific search with anyone easy:

http://showme.physics.drexel.edu/onsc/models/solventselector.php?csids=70660|21106059&limreact=1&limprod=0.01&bp=200&washes=0

It is even possible to use the service listing just one compound's CSID - this is useful for quickly comparing the measured solubilities with predictions from both models:

http://showme.physics.drexel.edu/onsc/models/solventselector.php?csids=70660

Berkeley Open Science Summit 2010 Notes

2010-08-02T18:53:00.000-04:00

I just returned from the Open Science Summit held at Berkeley July 29-31, 2010.

There certainly was an impressive list of presenters as well as attendees. Many of the talks were quite good, although several on the last day were more about closed collaborations than Open Science. During these presentations the assumption that patents are required to exploit discoveries in health care was repeated. This was in sharp contrast to the second day's session on gene patents, where IP protection was shown to stifle innovation and the exploitation of discoveries.

A refreshing exception to this pattern on the last day was Andrew Hessel's presentation on the Pink Army Cooperative. Andrew's strategy to cure cancer is based on the idea of customizing drugs for each individual affected by the disease. Since each drug is only applicable to one individual, the approach of expensive clinical trials doesn't apply. Since he is not interested in generating a profit from selling the drugs, IP protection also doesn't apply and allows him to make every part of the drug design process, including genetic analysis, publicly available. It wasn't clear if such an approach would be legal in the US but he did mention going to another country if necessary. Although he didn't currently have cancer, he did indicate that he might have need of this technology one day by pulling out a pack of cigarettes in the middle of his talk.

Unfortunately my panel on Open Data was canceled at the last minute due to time management problems (see FF discussion on how it happened). However, I did have a chance to generally catch up with old friends (Carmen Drahl, Joanna Scott, Cameron Neylon, Jack Park).

I also discussed some promising collaborations with several people:

1) CoLab. I spoke at length with DJ Sprouse and Casey Stark about their system for scientific collaboration. We will try to represent one solubility experiment from the ONS Challenge notebook and one organic synthesis experiment from the UsefulChem notebook to see how the information can be represented within CoLab. There may be some opportunities to visualize raw data in new ways - perhaps using non-Java tools to interact with JCAMP-DX spectra.

2) IPzero Principles. I continued a conversation with Lisa Green started with John Wilbanks and Thinh Nguyen at Creative Commons about coming up with a series of simple recommendations for ensuring that an Open Notebook can effectively prevent the patenting of inventions within an area of interest to the Open Science community.

3) Open Chemistry Reactions. I had the chance to discuss our Reaction Attempts database with Peter Murray-Rust over breakfast on Saturday. He also showed me how he is using Oscar to extract chemical reaction information from various documents. Peter suggested that we pool together our data for a demonstration in September at the London Science Online Conference. Reaction Attempts will cover the reactions done in the UsefulChem and the Todd group's Open Notebooks. Peter will extract information from both patents and Acta Crystallographica.

4) ChemTaverna. I was pleased to learn from Carole Goble that Taverna is extending its coverage to cheminformatics applications with the ChemTaverna project. I had just mentioned that we would be interested in revisiting Taverna for creating virtual libraries of organic compounds and filtering them based on predicted solubilities in various solvents. This would allow us to contribute cheminformatics workflows to MyExperiment. Carole put me in touch with the project leader Peter Li at the University of Manchester.

General Transparent Solubility Prediction using Abraham Descriptors

2010-07-25T18:18:00.001-04:00

Making solubility estimations for most organic compounds in a wide range of solvents freely available has always been a main long term objective for the Open Notebook Science Solubility Challenge. With current expertise and technology, it should be as easy to obtain a solubility estimate as it is now to get driving directions off the web.

Obviously this won't be attained purely by exhaustive measurements, although we have been focused on strategic measurements over the past two years. In parallel, we have been constantly evaluating the various solubility models out there for suitability.

Although there are several solubility models available for non-aqueous solvents, our additional requirement for transparent model building has proved surprisingly difficult to satisfy.

From this search, the Abraham solubility model [Abraham2009] floated to the top, with an important factor being that Abraham has made available extensive compilations of descriptors for solutes and solvents. In addition the algorithms used to convert solubility measurements to Abraham descriptors (a minimum of 5 different solvents per solute) has allowed us to generate our own Abraham descriptors automatically simply by recording new measurements into our SolSum Google Spreadsheet. These can be obtained in real time as well.

This approach permitted us to provide predictions for a limited number of solutes in a wide range of solvents and we have included these predictions in the past two editions (2nd and 3rd) of the ONS Challenge Solubility Book.

Coming at the problem from a different approach, Andrew Lang has also been trying to predict solubility using only open molecular descriptors, mainly relying on the CDK. Since our most commonly used solvent has been methanol, Andy recently generated a web service to predict solubility in that solvent.

By combining these two approaches, Andy has now created a modeling system that can not only generally predict solubility in a wide range (70+) of solvents - but it can also provide related data that can be used for modeling other phenomena such as intestinal absorption of a drug or crossing the blood-brain barrier.[Stovall 2007]

The idea is to use a Random Forest approach to select freely available descriptors to predict the Abraham descriptors for any solute. A separate service then generates predicted solubilities for a wide range of solvents based on these Abraham descriptors. I'm using the term "freely available" because - although the CDK descriptors and VCCLab services are open - the model requires 2 descriptors only available from ChemSpider (ultimately from ACD/Labs).

Here is an example with benzoic acid. As long as the common name resolves to a single entry on ChemSpider, it is enough to enter it and it automatically populates the rest of the fields, which are then used by the service to generate the Abraham descriptors.

Hitting the prediction link above will automatically populate the second service and generate predicted solubilities for over 70 solvents.

This approach of allowing people to access these components separately can be useful. It can be instructive to manually play with the Abraham descriptors directly to see how predicted solubilities are affected. There are also situations where one has experimentally determined Abraham descriptors for a solute and bypassing the descriptor prediction step is required.

However, for those who prefer to cut to the chase, a convenient web service is available where the common name (or SMILES) of the solute is entered and the list of available solvents appears as a drop down menu.

Now here is where I think the real payoff comes for accelerating science with openness. Andy has also created a web service that returns the predicted solubility in molar as a number from common names (or SMILES) for solute and solvent via the URL. For example click this for benzoic acid in methanol. The advantage here is that solubility prediction can be easily integrated as a web service call from intuitive interfaces such as a Google Spreadsheet to enable even non-programmers to make use of the data. Notice that the web service provided in the fourth column for the average of measured solubility values enables an easy way to explore the accuracy of specific predictions.

Such web services could also be integrated with data from ChemSpider or custom systems. If those who use these services feed back their processed data to the open web, it could take us a step closer to automated reaction design. For example consider the custom application to select solvents for the Ugi reaction. Model builders could also use the web services for predicted and measured solubility directly.

A while back we explored using Taverna for MyExperiment to create virtual libraries of SMILES. Unfortunately we ran into issues with getting the applications developed on Macs to run on our PCs. This might be worth revisiting as a means of filtering virtual libraries through different thresholds of predicted solubility.

Andy has described his model in detail in a fully transparent way - the model itself, how it was generated and the entire dataset can be found here. We would welcome improvements of the model as well as completely new models based on our dataset using only freely available tools.

It should be noted that when I use term "general" it refers to the ability for the model to generate a number for most compounds listed in ChemSpider. Obviously compounds that most closely resemble the training set are more likely to generate better estimates. Because of our synthetic objectives using the Ugi reaction we have mainly focused on collecting solubility data for carboxylic acids, aldehydes and amides either from new measurements or from the literature.

Another important point concerns the main intended application of the model: organic synthesis. Generally the range of interest for such applications is about 0.01 - 3M. This might be very different for other applications - such as the aqueous solubility of a drug, where distinctions between much lower solubilities may be important.

For a typical organic synthesis, a solubility of 0.001M or 0.005M will probably translate as effectively insoluble. This might be a desired property for a product intended to be isolated by filtration. On the other end of the scale knowing that a solubility is either 4M or 6M will not usually have an impact on reaction design. It is enough to know that a reactant will have good solubility in a particular solvent.

Given the above considerations for intended applications and the likelihood that the current model is far from optimized, the predictions should be used cautiously. We suggest that the model is best used as a "flagging device". For example, if a reaction is to be carried out at 0.5M, one may place a threshold at 0.4M for the predicted values of reactants during solvent selection, with the recognition that a predicted 0.4M may be an actual 0.55M. A similar threshold approach can be used for the product, where in this case the lowest solubility is desired. A practical example of this is the shortlisting of solvents candidates for the Ugi reaction.

Another example of flagging involves identifying the outliers in the model. These can be inspected for experimental errors and possibly remeasured. Alternatively outliers may shed light on the limitations of the model. For example we have found that the solubility of solutes with melting points near room temperature can be greatly underestimated by the current model. This may be an opportunity to develop other models which incorporate melting point or enthalpy of fusion.[Rohani 2008]

Although it is possible that better models and more data will improve the accuracy of the predictions, this can be true only if the training set is accurate enough. Based on conversations I've had with researchers who deal with solubility, reading modeling papers and our own experience with the ONS Challenge I am starting to suspect that much of the available data just isn't accurate enough for high precision modeling. Models using data from the literature are especially vulnerable I think. Take a look at this unsettling comparison between new measurements and literature values (not to mention the model) for common compounds.[Loftsson 2006] Here is a subset:

I have also made the point in detail for the aqueous solubility of EGCG. Could this be the reason that so many different solubility models using different physical chemistry principles have evolved and continue to co-exist?

The situation reminds me a lot of the discussions taking place in the molecular docking community.[Bissantz 2010] The differences in calculated binding energies are often small in comparison with the uncertainties involved. But docking can still be used as one tool among others to find drug candidates by flagging a collection of compounds above a certain threshold binding energy.

Resveratrol Thesis on Reaction Attempts

2010-07-21T10:59:00.000-04:00

A few days ago Andrew Lang suggested to Dustin Sprouse that he submit his thesis to the Reaction Attempts database. Like many undergraduates Dustin put in a lot of time and effort in doing experiments and writing up his results but didn't have quite enough time to obtain all that would have been required for a traditional publication.

A thesis is an unusual document within the context of scientific communication. Unlike a peer reviewed paper, it may contain a large number of "failed experiments" and a substantial amount of speculation. Although it is not quite as detailed as lab notebook, there is often plenty of raw data and details about how failed or ambiguous experiments proceeded.

In Dustin's case we felt that there was enough information provided to include his thesis in Reaction Attempts. In addition, his thesis was accepted by Nature Precedings, thus providing a convenient means of citation.

The first component of the Reaction Attempts project is to quickly abstract the most basic information from synthetic organic chemistry reactions. This includes the ChemSpiderIDs and SMILES from the reactants and target products and brief notes about conditions and outcomes. We are especially interested in failed or ambiguous experiments because these have almost no chance of being communicated and indexed in the traditional systems. When attempting to carry out a reaction, it can be just as useful to know what doesn't work - and more specifically how it doesn't work.

The second component of the project is dissemination. Because the information is encoded semantically, it can be automatically converted to both human and machine readable formats.

One human interface consists of a PDF book (also as a hard copy), with the option of selected reactions specified by listing CSIDs of reactants in the URL. For example Dustin's reactions can be presented selectively here. We also have a Reaction Explorer, where reactants or products can be selected from a dropdown menu or via a substructure search.

We also provide live XML feeds so that others can create applications easily from machine readable data. For example one could create reaction chains automatically, which will occur whenever we enter reactions from multi-step syntheses like Dustin's - based on the synthesis of resveratrol.

I know that Peter Murray-Rust has been very active in automatically abstracting information from chemistry theses. It would be interesting to see how that approach would work for this thesis, especially with the failed experiments. Reducing a page or two of text into only the most salient bits of information manually required a level of judgement that I imagine would be tricky to do automatically.

Secrecy in Astronomy and the Open Science Ratchet

2010-07-11T14:35:00.000-04:00

Probably because of the visibility of the GalaxyZoo project, I think several of my colleagues and I have been under the impression that astronomy is a somewhat more open field than chemistry or molecular biology. It was easy to rationalize such a position because patents are not an issue, as they clearly are in fields which rely more on invention than discovery. However, after reading "The Case for Pluto" by Alan Boyle, I am left with a much different impression.

This book does an excellent job of covering the recent debate over Pluto's designation as a true planet. A key trigger for this debate has been the discovery of dwarf planets with sizes very close to that of Pluto. However, these discoveries did not occur without controversy.

The story of the controversy regarding the discovery of Haumea is a particularly good example (starts on p. 108 of the book - a good summary also on Wikipedia). Starting in December 2004 Michael Brown at Caltech discovered a series of new dwarf planets. Instead of immediately reporting his team's discoveries, he worked in secrecy until July 20, 2005 when he posted an online abstract indicating the discoveries would be announced at a conference that September. However, on July 27, 2005 a Spanish team led by José Luis Ortiz Moreno filed a claim with the Minor Planet Center for priority in discovering one of these dwarf planets. This forced Brown's hand in disclosing his team's other discoveries within days - much sooner than he had anticipated.

Apparently this stirred up a great controversy in the community and officially no name was associated with the discovery, although the Spanish team's telescope at Sierra Nevada Observatory was recognized as the location of the discovery. However, Brown was allowed to select the name Haumea for the dwarf planet.

Even though the Minor Planet Center accepted Moreno's submission, most reports seem to side with Brown. The main argument is no less than academic fraud on Moreno's part because he accessed public telescope logs and found some of Brown's data. It was as simple as Googling the identifier that Brown inserted in his public abstract.

If Moreno had hacked into a private computer from Brown's team I can understand fraud. But is it fraud to access public databases? We chemists do that all the time - reading abstracts from upcoming conferences to try to glean what our competitors are up to. That hasn't stopped anyone from submitting a paper or patent.

Secrecy only works if everyone competing follows the same rules. If there is a rule that planet discoveries must be made at conferences or by formal publication then this could not have happened. Moreno's submission to the Minor Planet Center should have been rejected if such a rule existed. If there is a rule that telescope logs should not be accessed then why make them public and indexed on Google?

Now there may exist field specific conventions. I don't know what they are in the case of discoveries such as these but here is an interesting quote from Michael Brown's Wikipedia page:

When asked about this online activity, Ortiz responded with an email to Brown that suggested Brown was at fault for "hiding objects," and said that "the only reason why we are now exchanging e-mail is because you did not report your object."[3] Brown says that this statement by Ortiz contradicts the accepted scientific practice of analyzing one's research until one is satisfied that it is accurate, then submitting it to peer review prior to any public announcement. However, the MPC only needs precise enough orbit determination on the object in order to provide discovery credit, and Ortiz et al. not only provided the orbit, but "precovery" images of the body in 1957 plates.

It seems to me that there is a clash of what are the conventions in the field. Certainly the Minor Planet Center did not recognize the convention of peer review before public disclosure. They only required sufficient proof for the discovery.

One way to look at this story is that Moreno acted more openly than Brown by disclosing information before peer review. This action forced Brown to disclose scientific results much more quickly than he had anticipated.

In a sense this is a type of Open Science Ratchet. The actions of scientists that are most open set the pace for everyone else working on that particular project, regardless of their views on how secretive science should be.

Imagine how the scenario would have played out if one of the groups had used an Open Notebook. On December 28, 2004 everyone with a stake in the search for planets would have had the opportunity to know that a very significant find had been made. There were still details to work out - and the Brown group might not be the first to do all the calculations to completely characterize the discovery. Certainly it would affect what other researchers did - even if they were completely opposed to the concept of Open Science.

Essentially secrecy in this context is an all-or-nothing gamble. Everyone is free to not disclose their work until after peer reviewed publication. In some cases the discoverer will get full credit for the discovery and the complete analysis. But in other cases another group working in parallel will publish first and leave nothing to claim.

As scientists become more open, it is likely that their ability to claim sole priority for all aspects of a discovery will be reduced. However, they will retain priority for the observations and calculations that they made first.

The more open the science, the faster it happens. And because of the Open Science Ratchet, a few Open Scientists scattered across various fields could have a larger hand than expected in speeding up science.

Methanol Solubility Prediction Model 4 for Ugi reactions in the literature

2010-07-08T16:30:00.000-04:00

Since non-aqueous solubility measurements have not become part of the standard characterization of organic compounds, it is not surprising that all the data we have for Ugi products originate from measurements that we made on our own compounds.

Since methanol is our most common solvent, Andrew Lang has collected the measurements that we have with values from the literature for a range of compounds, including our Ugi products, to generate a web service returning a predicted solubility based on a submitted SMILES string. The model (Model 4) was derived from a Random Forest algorithm, using molecular descriptors supplied by the CDK and VCC.

It would be nice to be able to test the model's ability to predict what will happen if a Ugi reaction is carried out in methanol. Although the actual solubility of Ugi products in the literature is typically not reported, reading the experimental sections in papers can still provide some validation of the model in some cases.

For example, consider the following Ugi products synthesized recently by Lezinska (Tetrahedron 2010)

Note that these images represent the azide group not following the octet rule. It is necessary to represent the structure SMILES without charges because the CDK and VCC web services used by the model do not process charges correctly. Stereochemistry also cannot be used and this can be removed from the SMILES simply by deleting slashes. Thus for the two molecules above the SMILES to be submitted to the prediction web service are:

O=C(NC1CCCCC1)C(Cc2ccc(C)cc2)N(c4ccccc4C(=O)c3ccccc3)C(=O)C(Cc5ccccc5)N=N#N
AND
O=C(NC1CCCCC1)C(C(=O)c2ccccc2)N(Cc3ccc(C)cc3)C(=O)C(C)CCN=N#N

The predicted methanol solubilities are respectively 0.004 M and 0.03 M.

Now if we look at the details in the experimental section, both of these Ugi products were synthesized in methanol at a limiting reactant concentration of about 0.1 M. Even though this is much more dilute than the usual 0.5-2.0 M generally recommended for Ugi reactions (Domling 2000), the products still precipitate and can be filtered off. This is consistent with the predicted solubilities above and the model would have suggested ahead of time that methanol might be a good solvent for isolation of the products by precipitation.

So far these are just anecdotal results but it does illustrate that solubility models can be evaluated without explicit determination of solubility in the literature.

Reaction Attempts Explorer

2010-06-25T19:26:00.000-04:00

Two months ago I reported on the Reaction Attempts project and the availability of the summary as a physical or electronic (PDF) book. The basic idea behind the project is to collect organic chemistry reaction attempts reported in Open Notebooks. This would include not only successful experiments but also those which could be categorized as failed, ambiguous, in progress, etc.

The book was organized with reactants listed alphabetically. In this way one could browse through summaries of the types of reactions being attempted by different researchers on a reactant of interest. There might be information there (what to do or what to avoid) of some use for a planned reaction. At the very least one could contact the researcher to initiate a discussion about work that had not yet been published in the traditional system.

Andrew Lang has just created a web-based tool to explore the Reaction Attempts database in much more sophisticated ways.

Here are some scenarios of how one could use it. On the left hand side of the page is a dropdown menu containing an alphabetically sorted list of all the reactants and products in the database. Lets select furfurylamine.

This immediately informs us that there are 230 reactions involving furfurylamine and it lists the schemes for all these reactions upon scrolling down. That's still a bit hard to process so a second dropdown menu appears populated with a list of other reactants or products involved with furfurylamine.

We now select boc-glycine and that narrows our search to 145 reactions.

Selecting benzaldehyde from the third dropdown menu narrows the search further to 61 reactions.

The final dropdown menu contains a short list of only isocyanides and thus all represent attempted Ugi reactions. Selecting t-butyl isocyanide gives us 56 reactions.

That means that these same 4 components were reacted together 56 times. Looking at the various reaction summaries will show that some of these are duplicates for reproducibility and others vary concentration and solvent and the effect on yield is included. This particular reaction was in fact the subject of a paper on the optimization of a Ugi reaction using an automated liquid handler.

Now here is where the design of the Explorer comes in handy. We might want to ask if the reaction proceeds as well with the other isocyanides. All we have to do is switch the final dropdown menu to ask what happens when we go from t-butyl to n-butyl isonitrile. There is a single attempt of this reaction and it is "failed" in the sense that no precipitate was obtained from the reaction mixture. This doesn't mean that the reaction didn't take place - it might be that the Ugi product was too soluble. We can quickly inspect that the concentration and solvent are in line with conditions that allowed precipitation of the t-butyl derivative.

OK lets see what happens with n-pentyl isocyanide.

It looks like it behaves just like n-butyl isocyanide: another single non-precipitation event. What about benzyl isocyanide?

This time we do get the Ugi product from a single attempt. Note the lower yield compared to the t-butyl isocyanide under similar conditions.

What about with cyclohexyl isocyanide?

This time we hit an experiment in progress. A precipitate was obtained but it was not characterized. We can click on the link to the lab notebook page (EXP232) to learn more about how long it took for the precipitate to appear but there are not enough data to draw a definite conclusion about the successs of the reaction. However, based on the results from the other precipitates in this series it is probably encouraging enough to repeat and characterize the product.

There are other sources of information here. Clicking on the image of the Ugi product takes us to its ChemSpider entry. In this case the only associated data relates to this reaction attempt.

Lets look at another scenario: reactions involving aminoacetaldehyde dimethyl acetal.

In this case we find the intersection of two Open Notebooks. The first reaction comes from Michael Wolfle from the Todd group.

The second comes from Khalid Mirza from the Bradley group.

In order to learn more about the nature of the overlap we can use the substructure search capabilities of the Reaction Explorer. Simply click on the image of the acetal and the ChemSpider entry pops up. Now click on the copy button next to the SMILES for the compound.

Paste the SMILES into the SMARTS box of the Reaction Explorer.

We get 13 reaction attempts for this query - the two we found earlier and the rest corresponding to attempts by Michael Wolfle to synthesize praziquanamine.

We learn that one connection between these two notebooks involves different attempts at synthesizing praziquantel.

Hopefully this demonstrates the value of abstracting organic chemistry reaction attempts from Open Notebooks into a machine readable format. Contributions to the database require only the ChemSpider IDs of the reactants and product and a link to the relevant lab notebook page. Reaction schemes are automatically generated by the system. More on the Reaction Attempts project here.

IGERT NSF panel on Digital Science

2010-06-07T14:35:00.000-04:00

On May 24, 2010 I was part of a panel in Washington for the NSF IGERT annual meeting. As I mentioned previously, it is encouraging to find that funding agencies are paying more attention to the role of new forms of scholarship and dissemination of scientific information.

My co-panelists included Janet Stemwedel, who talked about the role of blogging in an academic career, Moshe Pritzker, who made a case for using video to communicate protocols in life sciences and Chris Impey, who demonstrated applications of clickers and Second Life in the classroom.

We only had 10 minutes each to speak so the presentations were basically highlights of what is possible. Still, it was enough to stimulate a vigorous discussion with the audience. There was a bit of controversy about the examples I used to demonstrate the limitations of peer review in chemistry. People can misinterpret what we are trying to do with ONS - it certainly doesn't include bringing down the peer review system (not that we could anyway). But we have to face the situation that peer review does not validate all the data and statements in a paper. It operates at a much higher level of abstraction. Providing transparency to the raw data should work in a synergistic way with the existing system.

My favorite part of the conference was easily Seth Shulman's talk on the "Telephone Gambit". Ever since reading his book, I have been using the story of how carefully reading Bell's lab notebook has forced us to revise the generally accepted notion of how the telephone was invented. Seth's presentation was truly captivating because he explained not only what was done but also what motives were at work to deceive and obfuscate. This cautionary tale is still very much relevant to science and invention today - and highlights how transparency can mitigate against this type of outcome.

IGERT Open Notebook Science

View more presentations from Jean-Claude Bradley.

Use of ONS to protect Open Research: the case of the Ugi approach to Praziquantel

2010-06-01T11:39:00.001-04:00

As we were collecting reactions from The Synaptic Leap for the Reaction Attempts project, Andrew Lang noticed that there might be a quick synthetic route to praziquantel via a Ugi reaction. I researched it further and found a paper (Kim et al 1998) where Ugi product 1 was indeed converted to racemic praziquantel via the Pictet-Spegler cyclization.

Using Beilstein Crossfire the only synthesis of 1 I found involves a multi-step amidation strategy. But this compound should be accessible in one step from commercially available starting materials via a Ugi reaction (shown above). Since all the starting materials are liquids we have some flexibility with solvent choice. Khalid first tried it in methanol EXP258 a few weeks ago but did not get a precipitate. He was going to monitor it by NMR next to see if the problem was high solubility of the Ugi product or with the reaction itself.

It was therefore with great interest that I read Mat Todd's report this morning on The Synaptic Leap that a German patent had been issued on this Ugi strategy to praziquantel. (TSL didn't provide a means of leaving a comment so I edited the page - which made me the author of that post but actually Mat wrote it)

I have often mentioned during my talks that Open Notebook Science could be used not only in a defensive manner to claim academic priority - but also as an offensive tactic to block patent applications. A company attempting to prevent the commercial exploitation of rival inventions has a few options. Where applicable, it can buy up an existing patent pool with the intention of sitting on it. For new inventions, it can do research and try to file patents before their competitors. But this is a costly process and it may make more sense to simply publish the inventions to create disclosed prior art, thereby blocking patent applications of their competitors.

But - as I and many others have discussed - the current publication system is not optimally suited for the purpose of simply disclosing and communicating science. Not only is it generally slow but the traditional article format requires a narrative of some sort - rarely can single experiments be published. This means that much (if not most) of research done by an individual or group will never be disclosed.

For these reasons I think that keeping an easily discoverable Open Notebook for projects designed to block patent submission by competitors makes a lot of sense - both economically and from a workflow perspective. Since researchers already have to keep a lab notebook, making it public doesn't impose the added time that writing an article or patent will require.

In this specific example of praziquantel we were too late. But if we had recorded this experiment a few years ago it might have worked to block Domling's patent. Now, it isn't clear to me that EXP258 would have been enough to do that. The strategy to make praziquantel via a Ugi reaction was clearly stated but the experiment was not conclusive. However, since Domling reported that methanol worked I am sure that we would have had the "reduced to practice" evidence in the notebook shortly.

Above I used a company as an example of a party motivated to disclose inventions to protect their interests. In our case it would not be a company but rather the entire Open Science community. It is in our best interest to keep our scientific territory as unencumbered by patents as possible. Keeping Open Notebooks might be one of the simplest means of ensuring that.

Consider a humanitarian organization that might want to manufacture praziquantel. I haven't researched it but presumably the Domling patent was filed in a number of countries beside Germany. In order to consider using the Ugi strategy, the organization would now have to deal with the patent holder. This might be the factor that makes this route untenable. Patents have proven to be problematic for humanitarian aid - even in the simple case of providing food.

But all is not lost. In addition to offering a simple 2-step synthesis of praziqantel, the Ugi route offers an easy way to make large libraries of analogs. Optimally we would like to work with someone who has experience with docking praziquantel. It might be interesting to screen not only the praziquantel analogs but also the uncyclized Ugi products themselves. When we did this for malarial enoyl reductase inhibitors (D-EXP005) we found that we did not need to cyclize to obtain compounds predicted to bind. This ultimately led to active compounds.

OpenSciNY Open Notebook Science Talk

2010-05-16T20:26:00.000-04:00

On May 14, 2010 I presented on Open Notebook Science at the OpenSciNY conference at the New York University Bobst Library. I introduced the topic by telling a few stories about how new forms of communication are affecting how we think about concepts like "scientific precedent", "peer review", "scientific publishing" and "scientific scholarship". At the end I spoke about archiving Open Notebook Science projects and showed the physical copies of both the Reaction Attempts and ONS Solubility Challenge books.

Margaret Smith did a wonderful job of organizing the conference with a very interesting line-up of speakers: Heather Joseph, Antony Williams, Elizabeth Brown and David Hogg. We formed break-out sessions at the end to discuss with the attendees concepts around Open Science. I was part of the session on Promoting Open Science.

The tone at this and other similar conferences I have attended recently is probably best described as cautiously optimistic and focused on what is possible. The Open Science movement - at least as far as it is reflected by the people I know - does not seem to be driven by zealots or idealists trying to get everyone to drink the cool-aid. It is just a bunch of people who see opportunities to do things in better ways as new tools become available - and they can't find a credible reason not to do them.

Check here on FriendFeed for updates about links to recordings, slides, etc.

My presentation below:

OpenSciNY Open Notebook Science

View more presentations from Jean-Claude Bradley.

The Scientist Article on Electronic Lab Notebooks

2010-05-07T17:17:00.000-04:00

Amber Dance has written an article in The Scientist (2010-05-01) Digital Upgrade: How to choose your lab’s next electronic lab notebook. This is basically a quick overview of different Electronic Lab Notebooks (ELNs) that should be helpful for people researching what is currently available in that space.

There was some coverage of Open Notebook Science and Steve Koch and I were quoted. Ironically my contribution appeared in the "Cons" section :)

Pros
The format is unconstrained—you can set up any categories, and as many users and pages, as you want—and fast to set up.
Open notebooking attracts collaborators. Koch counts three collaborations that wouldn’t have happened if he weren’t on OpenWetWare. And his students build professional networks well before they author a paper.

Cons
Wikis were not designed with scientific data in mind. For example, it’s hard to make a table, Koch says.
Open notebook science “does limit where you can send your work,” says Jean-Claude Bradley, a chemist at Drexel University in Philadelphia, who also uses an open wiki notebook. His lab sticks to journals that accept preprints.
Posting online voids international patent rights, although US patents are still possible.

In my opinion, one of the biggest "Pros" wasn't listed in that section: the free cost. (That was mentioned elsewhere though) When you see the costs of some of these other commercial systems, that has to be a factor for many people trying to make a decision.

If privacy is an issue wikis can certainly be made private, although I'm not sure if that is possible on OpenWetWare. It can be done for $5/month on Wikispaces, the wiki we use for lab notebooks - although then it wouldn't be Open Notebook Science.

Concerning Steve's Con of wikis being difficult to use to store data, that is true. However combining the use of a wiki with Google Spreadsheets has completely resolved that issue for us. With our ability to automatically export an archive of the notebook (as HTML) and spreadsheets (as XLS) into an integrated archive, the two platforms operate essentially as if they were a single system.

Visualizing Chemistry in Second Life ACS Talk Recording

2010-05-04T15:23:00.001-04:00

The American Chemical Society has processed the recording of the talk that Andrew Lang and I gave at the Spring 2010 ACS meeting in San Francisco on March 23, 2010:
Visualizing Chemistry in Second Life

ChemSpider SyntheticPages

2010-05-03T11:59:00.000-04:00

I recently mentioned the Reaction Attempts project, which aims to collect organic chemistry experiments - especially those that are "failed", in progress or somehow incomplete.

For reactions where the desired product has been obtained and fully characterized, ChemSpider SyntheticPages also offers a very convenient publication vehicle. As I mentioned previously there is a need for enabling the publication of single experiments, especially when these are unlikely to become part of a traditional article.

We are in the process of submitting suitable reactions from the UsefulChem project to CS|SP. This will require some re-formatting of procedures and characterization data as they currently appear in the lab notebook.

Here is an example of one of our Ugi reactions: SyntheticPage 406 (UCEXP176C)

A nice feature of these pages is the automatic rendering of 2D structures upon hovering on top of chemical names.

Here are a few more reasons to use ChemSpider SyntheticPages:

* ChemSpider SyntheticPages takes you directly to a procedure. When you get a hit - you get a procedure.
* ChemSpider SyntheticPages provides information that may not generally be found elsewhere, such as frequently encountered problems, trouble-shooting tips, the number of times the reaction has been carried out, scale-variation etc.
* ChemSpider SyntheticPages is the only interactive chemistry database. Information is constantly updated and validated by comments from the user community (Peer Review in the Public Domain™).
* ChemSpider SyntheticPages can provide you with the most up-to-date method, we aim for 95% of submissions to be processed within 48 hours of submission.
* ChemSpider SyntheticPages is free of charge.

[Disclaimer: I am a member of the editorial group at CS|SP]

The Synaptic Leap Experiments on Reaction Attempts

2010-05-02T20:37:00.000-04:00

Andrew Lang and I recently reported on the first edition of the Reaction Attempts book and database. Part of the motivation for this was to structure the experiments from the UsefulChem project in both a machine readable format and one that could be browsed as a physical copy. However, we also had in mind the easy integration of other open experiments, especially those labeled as "failed", since these are unlikely to be found by searching conventional reaction archives.

As a demonstration, we have added a series of experiments from The Synaptic Leap, which Michael Wolfle (working as a post-doc with Mat Todd) has posted. All of these reactions involve intermediates in the synthesis of praziquantel, which is a major focus of the Todd group. One group of these reactions involved the attempted synthesis of praziquanamine via a Pictet-Spengler cyclization. Most of these are failed attempts and one successful one.

Adding these experiments to Reaction Attempts was very simple - since the minimum information required is the ChemSpiderIDs (CSIDs) of all the reactants and the product, which a hyperlink to more details. We also added a few more details provided by Michael - such as the solvent, reaction conditions and outcome.

Andy has provided a simple mechanism to pull up all Reaction Attempts for a given reactant with the following url structure:

http://showme.physics.drexel.edu/onsc/databook/ucdatabook.php?reactants=9099925

The number at the end is the CSID for the reactant. Multiple reactants can be pulled from the database by adding more CSIDs separated by commas.

Successful runs in Reaction Attempts are identified with a green check mark:

Again the main idea here is not to exhaustively abstract all pertinent information for an experiment. Rather it is to connect up researchers who are working on similar reactions. Since it requires so little effort to come up with the minimum required information we are hoping to get contributions from other sources.

We will focus next on coming up with more sophisticated ways to retrieve information - such as substructure searching or by reaction type, solvent, etc. We will also periodically publish hard copies of future Reaction Attempts editions.

NMR integration web service expanded

2010-04-30T12:32:00.000-04:00

The ONS Challenge has extensively used a web service created by Andrew Lang to automatically calculate solubility from NMR spectra. One of the constraints of the service was that the JCAMP-DX file had to be deposited in a special folder on a server at Drexel.

Andy has now modified the script so that the JCAMP-DX file can be located anywhere on the internet. I have prepared a modified Google Spreadsheet to serve as a template for SAMS calculations (Semi-Automated Measurement of Solubility). Simply enter the url to the JCAMP-DX file in the appropriate column and fill in the ppm ranges and corresponding hydrogen numbers for the solvent and solute, and molecular weight and density data. (The predicted density of solids can be found on Chemspider). The concentration of the solute will then be automatically calculated based on an assumption of volume additivity.

The web service (which handles baseline correction) could be used for any other purpose involving the integration of spectra. Just make a copy of the Google Spreadsheet and modify.

Note that the JCAMP-DX files must be in XY format. If your instrument saves spectra in a compressed format they must be converted to XY. The desktop version of Robert Lancashire's JSpecView can be used to carry out the conversion.

This template spreadsheet also features a service in a cell to display the NMR spectrum by simply clicking on the link inside the cell. This is very handy because it obviates the need to create an HTML file which must normally accompany the JCAMP-DX file for viewing. Being able to quickly view a spectrum from a particular row within the Google Spreadsheet makes tracking data provenance very intuitive and errors easy to spot.

Reaction Attempts Book Edition 1 and UsefulChem Archive

2010-04-28T16:47:00.000-04:00

I am pleased to report that Andrew Lang and I have published the first edition of the Reaction Attempts book. It currently contains most of the Ugi reactions from the UsefulChem project and is associated with an April 27, 2010 snapshot archive of the entire UsefulChem project, including NMR spectra, spreadsheets, images and the entire lab notebook from Wikispaces.

At 582 pages the printing cost from LuLu amounts to $26.28. Not meant to replace electronic searches, it should prove to be a handy reference book for the lab to quickly browse through what was attempted for a given reactant, what the outcome was and the researcher involved.

We are hoping to include reaction attempts from other groups in future editions. More details can be found in the preface, reproduced below:

Reaction Attempts First Edition
Data Source: the UsefulChem project
Introduction
Open Notebook Science (ONS) refers to the practice of making the full contents of a laboratory notebook and all associated raw data files available in near real time.[1] This represents an opportunity for everyone to benefit from work in progress in an open research group. However, in order to make use of the information, it must be easily discoverable. A simple strategy to increase discoverability is redundancy over multiple communication platforms.

In another project - the Open Notebook Science Solubility Challenge[2] - we published non-aqueous solubility data in the form of physical and downloadable (PDF) books.[3] Although it is possible to search the solubility database using web query interfaces, exploration of a Google Spreadsheet, an XML feed, etc.[4], having a physical copy in the laboratory has proved to be very convenient in several instances. A similar format for reactions will also be useful.
The UsefulChem Project
UsefulChem started in 2005 as an organic chemistry Open Notebook Science project with a main goal of discovering new anti-malarial agents that can be prepared by simple and cheap syntheses.[5] Most of the reactions on UsefuChem are Ugi reactions, which involve the mixing of an amine, aldehyde, carboxylic acid and isonitrile in a solvent at room temperature generally for a few hours to days.[6] The multicomponent design of the Ugi reaction and the simple reaction conditions make it ideal for exploring large virtual libraries and selecting compounds of interest to make.[7]

Isolation of the Ugi products can be immensely simpler, cheaper and readily scalable if they precipitate in pure form from the reaction mixture. To this end, much of the research in the UsefulChem project focuses on reaction conditions that lead to this outcome.[8] This is in fact the origin of the ONS Solubility Challenge discussed above.[9]

The Reaction Attempts Database
In order to look for patterns in the reaction conditions which led to Ugi product precipitation, the CombiUgiResults Google Spreadsheet was set up.[10] Reactions indexed there can be sorted by precipitation outcome, solvent, reactant, concentration, etc. and links to the laboratory notebook pages can be followed for full details. However, this sheet is designed specifically for Ugi reactions and contains columns specifically for the aldehyde, amine, carboxylic acid and isonitrile.

In order to enable the tracking of other types of reactions, the information in the CombiUgiResults sheet was reformatted into two other sheets: ReactionAttempts[11] (containing reagents and reactants) and RXIDsReactionAttempts[12] (containing reaction conditions and results, such as solvent, concentration of limiting reactant, appearance of a precipitate, yield, etc.). The two sheets are connected via the use of a common ReactionID. This format permits the representation of any type of reaction, with an unlimited number of reactants and products.[13]

By definition, any Open Notebook Science project in a work in progress. The listing of a reaction in this database only means that the researcher attempted or is in the process of attempting it. Whatever the situation, a link to the laboratory notebook page is provided, where the most recent information is available. The philosophy used here is that partial information is always better than no information at all. Thus a researcher investigating the prior use a particular reactant in a Ugi reaction might find the report that a precipitate was obtained in methanol helpful for designing their own reactions, even if the characterization of the precipitate is still pending. At the very least, knowing that a certain researcher has at least attempted a similar reaction is enough information for initiating a discussion, which may lead to valuable insights.
Reaction Attempts on Chemspider
Although SMILES[14] are provided in the spreadsheets, the primary key to identify compounds is the ChemSpider ID (CSID)[15]. This allows us to render molecule images in the book automatically. In the case of the ONS Solubility Challenge book[3], use of the CSID enables a convenient way to calculate various descriptors for displaying values in the book.

In addition, the compounds in the Reaction Attempts database are indexed on ChemSpider as two Data Sources: ReactantsAttemptedReactions and ProductsAttemptedReactions[13]. In this way a substructure search for either reactants or products will identify indexed molecules. Clicking on the Syntheses tab in the ChemSpider record for a selected molecule will then reveal a list of hyperlinks to the relevant laboratory notebook pages.
Organization of the Book
In keeping with the layout of the ONS Solubility Challenge Book, the reactants are listed in alphabetical order. Each entry displays the list of reactions where the reactant was used. This includes a scheme with all reactants and product as well as key metadata: the researcher, reaction type, solvent, limiting reactant concentration, observation of a precipitate, comments and a reference (links to the laboratory notebook page).

In this edition, only Ugi reactions are included. The reaction schemes are laid out in the following order: carboxylic acid, amine, aldehyde and isonitrile. This should allow for easy comparison between schemes within a given record. Reactions where the Ugi product was isolated and characterized are marked with a green check and the percent yield is noted. Since the Ugi products do not have simple common names, they are not included as separate entries. However, all reactions where the synthesis of a specific Ugi product was attempted can be found by looking up the entries for any of the four reactants.

Although this compilation is not exhaustive, it does cover the vast majority of reactions in the UsefulChem project to date. Future editions will include other reactions from UsefulChem and other sources.
Archive
This edition is linked to the UsefulChem data archive (ZIP)[16], (DVD)[17] and interactive hosted archive format[18], ReactionAttempts (XLS)[19] and RXIDsReactionAttempts(XLS)[20] taken on 2010-04-27.
References
1. Open Notebook Science Wikipedia Entry http://en.wikipedia.org/wiki/Open_Notebook_Science
2. Open Notebook Science Solubility Challenge Wiki http://onschallenge.wikispaces.com
3. Bradley, J.-C. First Edition of ONS Solubility Challenge Book UsefulChem Blog (2009)
http://usefulchem.blogspot.com/2009/12/first-edition-of-ons-solubility.html
4. Open Notebook Science Solubility Challenge List of Experiments page http://onschallenge.wikispaces.com/list+of+experiments
5. UsefulChem Wiki http://usefulchem.wikispaces.com
6. Ugi Reaction Wikipedia Entry http://en.wikipedia.org/wiki/Ugi_reaction
7. Dömling, A., & Ugi, I. (2000). Multicomponent Reactions with Isocyanides. Angewandte Chemie International English Edition, 39(18), 3168-3210. http://www3.interscience.wiley.com/journal/73500473/abstract.
8. UsefulChem List of Experiments http://usefulchem.wikispaces.com/All+Reactions
9. Bradley, J.-C. Open Notebook Science Challenge UsefulChem Blog (2008)
http://usefulchem.blogspot.com/2008/09/open-notebook-science-challenge.html
10. CombiUgiResults Google Spreadsheet http://spreadsheets.google.com/ccc?key=plwwufp30hfpUERhse9y5Kw
11. ReactionAttempts Google Spreadsheet
http://spreadsheets.google.com/ccc?key=0Ak1R8T6wt4YQdG9NejNLcDNUMkVBVURGM01TR0NxdXc
12. RXIDsReactionAttempts Google Spreadsheet
http://spreadsheets.google.com/ccc?key=0Ak1R8T6wt4YQdGVENVFMWjdzaGd2REJTTnA4RG5vblE
13. Bradley, J.-C. Reaction Attempts on ChemSpider UsefulChem Blog (2010)
http://usefulchem.blogspot.com/2010/03/reaction-attempts-on-chemspider.html
14. SMILES Wikipedia Entry http://en.wikipedia.org/wiki/Simplified_molecular_input_line_entry_specification
15. ChemSpider Web Site http://www.chemspider.com/
16. UC archive Drexel server (ZIP) http://showme.physics.drexel.edu/usefulchem/archives/usefulchem2010-04-27.zip
17. UC archive on lulu.com (DVD) http://www.lulu.com/product/dvd/usefulchem-archive/10791847
18. UC interactive hosted format http://showme.physics.drexel.edu/usefulchem/archives/usefulchem2010-04-27/All%20Reactions.html
19. Bradley, J.-C.; Lang, A.. Reaction Attempts Reactants and Products. UsefulChem. 2010-04-27.
URL:http://spreadsheets.google.com/pub?key=toMz3Kp3T2EAUDF3MSGCquw&output=xls. Accessed: 2010-04-27.
(Archived by WebCite® at http://www.webcitation.org/5pIsFEbT9)
20. Bradley, J.-C.; Lang, A.. Reaction Attempts RXIDs. UsefulChem. 2010-04-27.
URL:http://spreadsheets.google.com/pub?key=teD5QLZ7shgvDBSNp8DnonQ&output=xls. Accessed: 2010-04-27.
(Archived by WebCite® at http://www.webcitation.org/5pIs2eh62)

ONS Books Wiki

2010-04-20T19:44:00.000-04:00

I recently reported on our use of Nature Precedings to archive different editions of the ONS Solubility Challenge book. One of the advantages is that Precedings automatically alerts visitors if more recent editions exist.

However, today I learned that there is a glitch to this system: it is not possible to link individual versions on Precedings to a corresponding book edition on LuLu. That means that if you find yourself on the Nature Precedings entry and want to order the book from LuLu it isn't obvious at all how to do so.

To resolve this issue once and for all I just created a wiki page (ONSbooks.wikispaces.com) to track every edition of the book. This is actually better because I can also provide links to all the available data archives and blog posts corresponding to each edition.

This is also the page where we will keep track of every edition of other Open Notebook Science books. The next one to be published shortly is for the UsefulChem project.

Scientists Embrace Openness Article in Science Careers

2010-04-08T14:15:00.000-04:00

Chelsea Wald just published an article in Science Careers: Scientists Embrace Openness (April 9, 2010). She interviewed several people in the Open Science movement including Jonathan Eisen, Steve Koch, Anthony Salvagno, Carl Boettiger and myself.

The article covers Open Notebook Science, Open Data and associated themes. I think it presents a view of the most commonly discussed advantages and disadvantages very well.

One section was particularly relevant to an issue I recently posted about - (and discussed on FriendFeed):

Open Notebook Science advocates claim that being open may protect a scientist's ideas rather than exposing them to theft. Newton's decision to conceal his findings within an anagram made it harder for him to prove priority over rival Gottfried Leibniz. Open Notebook scientists say all they need to do is point to their open notebooks to show that they had an idea or found a result first.

ONS t-shirts from Zazzle

2010-04-06T18:18:00.000-04:00

Inspired by Graham Steel, I just received my t-shirt with an Open Notebook Science Logo and a picture of our crystal on the cover of our ONS Solubility Challenge book.

I was going to set up an ONS store but Zazzle does not permit zero royalties (don't see the logic there). But making up t-shirts on Zazzle is super simple - just grab a logo of your choice from the ONSclaims wiki.

Any other pic is your choice - this is the crystal from UCEXP150C

You can also order all kinds of other personalized things, including coffee cups.

Bipolar Electrodeposition of CdS: Scientific Results in Limbo?

2010-04-02T17:41:00.001-04:00

There has been a lot of discussion about the fear of getting "scooped" as a reason to be weary of using new scientific publication vehicles.

These conversations can be somewhat frustrating since people don't necessarily use the same definition of that term. Even dictionaries don't use the same language. For example, Dictionary.com has:

to get the better of (other publications, newscasters, etc.) by obtaining and publishing or broadcasting a news item, report, or story first: They scooped all the other dailies with the story of the election fraud.

Wiktionary has:

To learn something, especially something worthy of a news article, before (someone else). The paper across town scooped them on the City Hall scandal.

Depending on the definition used, one could argue that in the story I'm going to tell I got scooped or I did the scooping. Some people use the term to imply that a malicious act has taken place. The classic scenario is that one would blog about their research and a nefarious individual would appropriate their results and submit as their own for publication in a peer reviewed journal.

That isn't scooping - it is fraud - and I want to be clear that this is not what I am suggesting happened here.

Two months ago I was asked to review an article for the ACS journal Langmuir. Before 2005 one of my main research areas was bipolar electrodeposition and so I still get asked periodically to review papers in that field.

Not only was this paper in that area but it reported on exactly the same experimental design we had previously reported: the bipolar electrodeposition of cadmium sulfide. The solvent, reagents and substrate were different but it was the same material made by the same process.

Although 2 of our papers were listed in the references the key report was not. I noted this in my review but I was surprised to find that the paper was published without that correction. I contacted the editor of Langmuir to find out what happened. I thought perhaps the authors disagreed with some technical issue in our report.

But what actually happened is that the authors requested that the reference be included in the supplementary data section instead of the regular reference section of the article because it was not a peer-reviewed article. The editor thought that was a reasonable request and complied.

I was quite surprised by this because Langmuir - or ACS journals in general - do not have a formal policy on requiring references to be peer-reviewed. In fact, a quick search for "unpublished results" on Langmuir reveals many articles which use that as an acceptable reference.

I could understand not wanting to cite a blog post with unsubstantiated claims but the document in question is very thorough - it includes a systematic review of prior art, detailed experimental description and characterization data.

This is actually an example of a "SMIRP Knowledge Product". It is a publication device that I used to make public single experimental results from work that was recorded by my research group in the SMIRP Knowledge Management System we used at the time as a laboratory notebook.

The system was built on interlinked modules designed to produce "Knowledge Products" based on a combination of manual and automated workflows. For example, the module generating reviews of prior art was based on "Knowledge Filters" uncovering the novelty of the experiment in question by filtering precedents for relevant aspects.

In the case of "Bipolar Electrodeposition of Cadmium Sulfide onto a Tip of a Carbon Nanotube", the relevant knowledge filters were "Bipolar Electrodeposition", "Electrodeposition onto Carbon Nanotubes" and "Electrodeposition Approaches to Synthesize Cadmium Sulfide". Other modules generated the experimental description, results, discussion, conclusion and reference sections. In this way, not only could the experiment be fully documented but its context within the field could be extremely well defined in a systematic way.

With a workflow to create these knowledge products we still needed a way to communicate them. Back in 2003 options were far more limited than they are now. But luckily (or so we thought) at this time Elsevier was running the Chemistry Preprint Server. They offered a place to upload documents such as these and provided a way of citing them. We used the recommended citation format aggressively, including peer-reviewed articles such as this one from Springer.

However, attempting to access these documents today using the official links gives this as a result:
In what is probably one of the worst scientific publisher PR moves in recent memory, Elsevier broke all the hyperlinks they told their authors to use for citations. If you do some research you will find that the documents are still available from http://www.sciencedirect.com/preprintarchive but you have to register to even perform a search to find them! This requirement removes them from indexing by Google. Coupled with the broken citation links these documents are now very far removed from likely discovery.

The story would have ended there were it not for redundancy. I also uploaded copies to Drexel's institutional repository (DSpace), which are happily very well indexed by Google - and perhaps more critically - by Google Scholar. I had not fully appreciated the value of institutional repositories until I noticed that they are treated by some important databases as collections of scholarly works.

So what are the lessons for all the stakeholders?

For those who have scientific results that can be published as articles and MUST be published in ACS journals - send your manuscripts in. If you post them on your institutional repository first they may end up in limbo -they DO qualify as publications preventing you from submitting them as manuscripts to ACS journals - and they may NOT qualify as publications when you try to cite them in ACS journals.

But what about scientific result that cannot be published as manuscripts. The Knowledge Products are unlikely to be accepted by regular journals for several reasons. First they communicate only a single experimental result. Articles generally require narratives. Second, if some of them do get published in traditional journals, there will be copyright conflicts. The Knowledge Filters for the review of prior art will be identical for similar experiments. For example the Knowledge Product for the "Bipolar Electrodeposition of CdS on one Tip of a Carbon Nanotube" Will have identical prior art to the "Bipolar Electrodeposition of Cd on one Tip of a Carbon Nanotube" except for the section of the electrodeposition of Cd or CdS. And no - I don't think it is a good use of my time to move words around for every document to get around copyright issues.

Some of the Knowledge Products were incorporated into full articles when it made sense. But many, including the one under discussion here was not. So publishing this work as part of a full article was never even an option. There are so many scientific results like this that fall into that kind of limbo. Even today there are no really good publication vehicles for these types of results - besides institutional repositories. PLoS ONE might come to mind as an option but I don't think it fits their mandate to publish single experiments like this. And if they did it would be extremely expensive if they did not waive author fees every time. ChemSpider Synthetic Pages and similar initiatives might work for organic chemistry but this is materials science.

Considering all of these difficulties over the years is really the main motivation behind our migration away from a login based system like SMIRP to our adoption of Open Notebook Science based on a wikis and blogs, which are very efficiently indexed in real time by Google and thus easily discoverable without additional formatting work.

For publishers and authors, do you really think it is in your best interest to have a statement in the introduction about prior art say "To our knowledge, reports of bipolar electrodeposition of compounds have not been previously published." when a simple Google search shows that is not the case for the compound you are electrodepositing? I suppose the argument is that the term "published" is used with the technical interpretation of being "published under peer-review". It would have been better to at least make that explicit to avoid confusion. But the bottom line is that someone wanting to perform bipolar electrodeposition of cadmium sulfide will quickly find both reports and will learn two ways of doing it.

Beer Chemistry Quiz on ChemTiles

2010-04-01T14:00:00.001-04:00

During the Chemical Information Retrieval course I taught in the Fall of 2009, Alex Bilinski did a project on the chemistry of beer. He created a set of images with information that is either true or false in any context.

I just added these images of beer chemistry to the ChemTiles game, which I am actively using in my current Organic Chemistry I (CHEM241) course. Andrew Lang has made it very easy to add content to the game by simply uploading to a Flickr group. The category is determined by the Flickr tag.

I won't be testing my organic students on beer but they might find it fun to play that category. Alex wrote a fascinating report "Beer Flavor Compounds and Detection Methods" that can be used as a study guide for the quiz.

My students are currently competing on the topics of Lewis structures, hybridization, Newman projections and nomenclature. They simply need to sign in by entering "contest1" for the group name. The student in my class with the highest score for the contest1 group at the end of class (10:50 AM) on April 9, 2010 will win an organic chemistry textbook. As I have done in previous classes I'll run a few of these contests over the term with increasing amounts of course content.

It turns out that the ChemTiles game is very convenient to play on a Droid phone (and presumably on an iPhone although I have not checked that yet). For many students this might be a preferred way to review material before tests when on the move. I'll find out at the end of the term.

Back from ACS San Francisco Meeting 2010

2010-03-28T17:07:00.001-04:00

Last week I co-hosted a symposium with Noel O'Boyle and Andrew Lang on "Visual Analysis of Chemical Data" at the American Chemical Society meeting in San Francisco (March 22-23, 2010). The ACS recorded almost every talk in our symposium and I'll provide the link here when available (they tell me mid April).

Liz Dorland kicked off the symposium with a great keynote presentation covering effective visualization in a number of fields and the special challenges faced in chemistry. There were several talks about QSAR and I particularly enjoyed Edmund Chapness who incorporated the visualization of confidence in predictions with an intuitive colored molecule map. Geoff Hutchison gave an informative overview of Avogadro.

Perhaps the biggest revelation was the "iTunes for Cheminformatics" project by NIH researcher Ajit Jadhav (leading the team which includes Rajarshi Guha). The alpha version will be available for testing on April 5, 2010 and many of us are eagerly anticipating being able to give it a spin. From what I understand the system will automatically be able to identify scaffolds (fragments) in a collection of molecules and make it easy to search for and filter assay results.

Carmen Drahl covered in minute by minute detail announcements about new drug candidates on Twitter. Following the FriendFeed feed for the conference flagged a very interesting post about a Cold Fusion Symposium that was being held. In spite of the notorious lack of wireless availability at ACS conferences, attendees seem to be making due with accessing their social networks via their cell phone devices.

Andrew Lang and I spoke about Visualizing Chemistry in Second Life - our slides are here - hopefully I'll be able to post the recordings as well soon.

Visualizing Chemistry in Second Life ACS SP2010

View more presentations from Jean-Claude Bradley.

Education 2.0: Leveraging Collaborative Tools for Teaching

2010-03-27T10:34:00.000-04:00

On March 25, 2010 I presented at the Drexel E-Learning 2.0 Conference on "Education 2.0: Leveraging Collaborative Tools for Teaching". It was an opportunity to update my slides with what I did and learned from the Chemical Information Retrieval course I taught over the Fall 2009 term.

I described using a wiki to organize course content and to allow students to contribute useful resources. Their assignments were also designed to be useful to other students in the class as well as to the general library and chemistry community.

I covered using wikis and other collaborative tools to mentor students doing laboratory research with Open Notebook Science. At the end I provided a quick overview of using games and Second Life for educational purposes.

Education 2.0: Leveraging Collaborative Tools for Teaching

View more presentations from Jean-Claude Bradley.