CSA Trust Grant – Call for Proposals

Applications Invited for CSA Trust Grant for 2017

The Chemical Structure Association (CSA) Trust is an internationally recognized organization established to promote the critical importance of chemical information to advances in chemical research.  In support of its charter, the Trust has created a unique Grant Program and is now inviting the submission of grant applications for 2017.

Purpose of the Grants

The Grant Program has been created to provide funding for the career development of young researchers who have demonstrated excellence in their education, research or development activities that are related to the systems and methods used to store, process and retrieve information about chemical structures, reactions and compounds.  One or more Grants will be awarded annually up to a total combined maximum of ten thousand U.S. dollars ($10,000).  Grantees have the option of payments being made in U.S. dollars or in British Pounds equivalent to the U.S. dollar amount. Grants are awarded for specific purposes, and within one year each grantee is required to submit a brief written report detailing how the grant funds were allocated. Grantees are also requested to recognize the support of the Trust in any paper or presentation that is given as a result of that support.

Who is Eligible?

Applicant(s), age 35 or younger, who have demonstrated excellence in their chemical information related research and who are developing careers that have the potential to have a positive impact on the utility of chemical information relevant to chemical structures, reactions and compounds, are invited to submit applications.  While the primary focus of the Grant Program is the career development of young researchers, additional bursaries may be made available at the discretion of the Trust.  All requests must follow the application procedures noted below and will be weighed against the same criteria.

Which Activities are Eligible?

Grants may be awarded to acquire the experience and education necessary to support research activities; e.g. for travel to collaborate with research groups, to attend a conference relevant to one’s area of research (including the presentation of an already-accepted research paper), to gain access to special computational facilities, or to acquire unique research techniques in support of one’s research. Grants will not be given for activities completed prior to the grant award date.

Application Requirements

Applications must include the following documentation:

  1. A letter that details the work upon which the Grant application is to be evaluated as well as details on research recently completed by the applicant;
  2. The amount of Grant funds being requested and the details regarding the purpose for which the Grant will be used (e.g. cost of equipment, travel expenses if the request is for financial support of meeting attendance, etc.). The relevance of the above-stated purpose to the Trust’s objectives and the clarity of this statement are essential in the evaluation of the application);
  3. A brief biographical sketch, including a statement of academic qualifications and a recent photograph;
  4. Two reference letters in support of the application.  Additional materials may be supplied at the discretion of the applicant only if relevant to the application and if such materials provide information not already included in items 1-4.   A copy of the completed application document must be supplied for distribution to the Grants Committee and can be submitted via regular mail or e-mail to the Committee Chair (see contact information below).

Deadline for Applications

Application deadline for the 2017 Grant is March 31, 2017. Successful applicants will be notified no later than May 9, 2017.

Address for Submission of Applications: 

The application documentation can be mailed via post or emailed to:  Bonnie Lawlor, CSA Trust Grant Committee Chair, 276 Upper Gulph Road, Radnor, PA 19087, USA.  If you wish to enter your application by e-mail, please contact Bonnie Lawlor at chescot@aol.com prior to submission so that she can contact you if the e-mail does not arrive.

Endnote XML to HTML or LaTeX

Over the last few years I’ve been maintaining my publication list as a BibTeX file, managed by BibDesk. This is handy when writing papers, but it’s also useful to use this data to keep my CV updated or generate a publications page. Since BibDesk can export to Endnote XML format, I put together a simple Python script to process that to HTML or LaTeX. The latter assumes that you’re going to include the generated LaTeX file in a document that employs the CuRve package. The output is designed according to my preferences, but it’s easily modifiable.

The code is available at https://github.com/rajarshi/genpubs

Freedom from the IF: Impact Neutral Publishing

I came across a post from Jan Jensen a few months ago about a GRC meeting that he had attended. What caught my eye however, was his comment on “impact neutral” publishing. Specifically, he mentions

For me “impact neutrality” has become just as important as OA. It is so very liberating to just write down what I did and what I found rather than trying to put everything in the best possible light with elaborately constructed “technically-correct-but-not-really-telling-the-whole-story” paragraphs.

As a methods person myself, this resonated with me, and while not always feasible, I hope to be able to make some progress towards this form of publishing in the coming year.

So what does this mean? Essentially, you publish your work in the journal with the best fit, irrespective of impact factor (IF) or other measures of journal importance. By bypassing importance metrics it allows one to consider other, more relevant parameters such as topical fit and accessibility. Why is this approach useful? First, IF measures impact of a journal, and as a result, all work in a high IF venue is not necessarily impactful and conversely, work in low IF venue is not necessarily non-impactful. Second, an impact neutral publication can be a more honest description of what was done, since there’s less need to put a spin to justify impact. Third, it can avoid time spent in the journal funnel.

Importantly, impact neutral publication doesn’t imply poorly written or run-of-the-mill papers. A story still needs to be told in a clear and succinct fashion. In the end, publication is about letting people know what you did. As opposed to impressing people by what you did.

So, there are definitely benefits to this view of publishing. Is it for everybody? Ideally yes, but in todays climate, it doesn’t always work out. Indeed, this thread highlights the issues with asking people to ignore IF. It works well if judgement is not important/irrelevant (tenured faculty). In addition, there are groups such as government labs, for whom IMO impact should not be a factor, that could follow this publication policy. Of course, it is also true that much work is done by groups and within such a setting, different members will have different needs and agendas. So arbitrarily forcing impact neutral publication is not always feasible.

What are the downsides to this approach to publishing? For early career researchers and people hunting for money (aka grants), it is obvious – hiring and funding committees, unfortunately, do look at impact factors in many cases. While some people are pushing for changes, we’re not there yet. Having said that, what is the effect on the work itself that is published in this form? The primary effect is that it goes unnoticed or ignored or considered poor quality due to venue. In addition, such work may not benefit from popular press. Both these outcomes are unfair, but given the information overload of todays world, not unexpected.

So how does one address these drawbacks? There are two levels to this – at the individual level, the use of Twitter, blogs and other social media can help spread the word of your work. As you might expect this approach publicizes the work within your topical community. To break out of this sphere requires “network effects” and is non-trivial to achieve. However, the scientific community should also address this by way of cultural changes. Given that different fields have different cultures and policies, it’s unreasonable to expect every scientist to accept or even attempt these changes. But when certain fields are open to change and have people championing this (and other) approaches to publications, I believe that the community (which in reality are the senior scientists sitting on committees and holding the reigns) should keep an open mind and seriously consider the benefits to impact neutral publications.

Deep Learning in Chemistry

Deep learning (DL) is all the rage these days and this approach to predictive modeling is being applied to a wide variety of problems, including many in computational drug discovery. As a dilettante in the area of deep learning, I’ve been following papers that have used DL for cheminformatics problems, and thought I’d mention a few that seemed interesting.

An obvious outcome of a DL model is more accurate predictions, and as a result most applications of DL in drug discovery have focused on the use of DL models as more accurate regression or classification models. Examples include Lusci et al [2013], Xu et al [2015] and Ma et al [2015]. It’s interesting to note that in these papers, while DL models show better performance, it’s not consistent and the actual increase in performance is not necessarily very large (for the effort required). Eakins [2016] has reviewed the use of DL models in QSAR settings and more recently Winkler & Le [2016] have also briefly reviewed this area.

However, simply replacing one regression method with another is not particularly interesting. Indeed, as pointed by several workers (e.g., Shao et al [2013]) input descriptors, rather than modeling method, have greater effect on predictive accuracy. And so it’s the topic of representation learning that I think DL methods become interesting and useful in the area of cheminformatics.

Several groups have published work on using DL methods to learn a representation of the molecular structure, directly from the graph representation. Duvenaud et al [2016] and Kearnes et al [2016] both have described these approaches and the nice thing is that this alleviates the need to choose and select features a priori. The downside is that the learned features are optimal in the context of the training data (thus necessitating large training sets to allow for learned features that are generalizable). Interestingly, on reading Kearnes et al [2016], the features that are learned by the DL model are conceptually similar to circular fingerprints. More interestingly, when they built predictive neural network models using the learned representation, the RMSE was not significantly different from a random forest model using circular fingerprints. Of course, the learned representation is driven by the architecture of the DL model, which was designed to look at atom neighborhoods, so it’s probably not too surprising that the optimal representations was essentially equivalent to a circular fingerprint. But one can expect that tweaking the DL architecture and going beyond the molecular graph could lead to more useful representations. Also, this paper very clearly describes the hows and whys of designing a deep neural network architecture, and is useful for someone interested in exploring further.

Another interesting development is the use of DL to learn a continuous representation of a molecular structure, that can then be modified (usually in a manner to vary some molecular property) and “decoded” to obtain a new chemical structure with the desired molecular property. This falls into the class of inverse QSAR problems and Gomez-Bombarelli et al [2016] present a nice example of this approach, where gradient descent is used to explore chemical space defined by the learned continuous representation. Unfortunately the chemistry represented by the generated structures has several problems as described by Derek Lowe. While this problem has been addressed before (e.g., Wong et al [2009] with SVM, Miyao et al [2016], Skvortsova et al [1993]), these efforts have started with pre-defined feature sets. The current works key contribution is the ability to generate a continuous chemical space and I assume the nonsensical regions of the space could be avoided using appropriate filters.

Winkler & Le [2016] recently reported a comparison of deep and shallow neural networks for QSAR regression. Their results and conclusions are similar to previous work. But more tantalizingly, they make the claim that DNN’s may be better suited to tackle the prediction of activity cliffs. There has been some work on this topic (Guha [2012] and Heikamp et al [2012]) but given that activity cliffs are essentially discontinuities in a SAR surface (either fundamentally or by choice of descriptors), traditional predictive models are unlikely to do well. Winkler & Le point to work that suggests that activity cliffs may “disappear” if an appropriately high dimensionality descriptor space is used, and conclude that learned representations via DL may be useful for this. Though I don’t discount this, I’m not convinced that simply moving to higher dimensional spaces is sufficient (or even necessary) – if it were, SVM‘s should be good at predicting activity cliffs. Rather, it’s the correct set of features, that captures the phenomenon underlying the cliff, that are necessary. Nonetheless, Winkler & Le [2016] raise some interesting questions regarding the smoothness of chemical spaces.

A Report from a Stranger in a Strange Land

I just got back from ACoP7, the yearly meeting of the International Society of Pharmacometrics (ISoP). Now, I don’t do any PK/PD modeling (hence the “strange land”) but was invited to talk about our high throughput screening platform for drug combinations. I also hoped to learn a little more about this field as well as get an idea of the state of quantitative systems pharmacology (QSP). This post is a short summary of some aspects of the meeting and the PK/PD field that caught my eye, especially as an outsider to the field (hence the “stranger”).

The practice of PK/PD is clearly quite a bit downstream in the drug development pipeline from where I work, though it can be beneficial to keep PK/PD aspects in mind even at the lead discovery/optimization stages. However I did come across a number of talks and posters that were attempting to bridge pre-clinical and clinical stages (and in some cases, even making use of in vitro) data. As a result the types of problems being considered were interesting and varied – ranging from models of feeding to predict weight loss/gain in neonates to analyzing drug exposure using mechanistic models.

A lot of PK/PD problems are addressed using model based methods, as opposed to machine learning methods (see Breiman, 2001). I have some familiarity with the types of statistics used, but in practice much of my work is better suited for machine learning approaches. However, I did come across nice examples of some methodologies that may be useful in QSAR type settings – including mixed effect models, IRT models and Bayesian methods. It was also nice to see a lot of people using R (ISoP even runs a Shiny server for members’ applications) and companies providing R solutions (e.g., Metrum, Mango) and came across a nice poster (Justin Penzenstadler, UMBC) comparing various R packages for NLME modeling. I also came across Stan, which seems like a good way to get into Bayesian modeling. Certainly worth exploring nore.

The data used in a lot of PK/PD problems is also qualitatively (and quantitatively) different from my world of HTS and virtual screening. Datasets tend to be smaller and noiser, which are challenging to model (hence less focus on purely data driven, distribution-free M/L methods). A number of presentations showed results with quite wide CI’s and significant variance in the observed properties. At the same time, models tend to be smaller in terms of features, which are usually driven by the disease state or the biology being modeled. This is in contrast to the 1000’s of descriptors we deal with in QSAR. However, even with smaller feature sets I got the impression that feature selection (aka covariate selection) is a challenge.

Finally, I was interested in learning more about QSP. Having followed this topic on and off (my initiation was this white paper), I wasn’t really up to date and was a bit confused between QSP and phsyiologically based PK (PBPK) models, and hoped this meeting would clarify things a bit. Some of the key points I was able to garner

  • QSP models could be used to model PK/PD but don’t have to. This seems to be the key distinction between QSP and PBPK approaches
  • Building a comprehensive model from scratch is daunting, and speaking to a number of presenters, it turns out many tend to reuse published models and tweak them for their specific system. (this also leads one to ask what is “useful”?)
  • Some models can be very complex – 100’s of ODE‘s and there were posters that went with such large models but also some that went with smaller simplified models. It seems that one can ask “How big a model should you go for to get accurate results?” as well as “How small a model can you get away with to get accurate results?“. Model reduction/compression seems to be an actively addressed topic
  • One of the biggest challenges for QSP models is the parametrization – which appears to be a mix of literature hunting, guesswork and some experiment. Examples where the researcher used genomic or proteomics data (e.g. Jaehee Shim, Mount Sinai) were more familiar to me, but nonetheless, daunting to someone who would like to use some of this work, but is not an expert in the field (or a grad student who doesn’t sleep). PK/PD models tend to require fewer parameters, though PBPK models are more closer to QSP approaches in terms of their parameter space.
  • Where does one find models and parameters in reusable (aka machine readable) formats? This is an open problem and approaches such as DDMoRE are addressing this with a repository and annotation specifications.
  • Much of QSP modeling is done in Matlab (and many published models are in the form of Matlab code, rather than a more general/abstract model specification). I didn’t really see alternative approaches (e.g., agent based models) to QSP models beyond the ODE approach.
  • ISoP has a QSP SIG which looks like an interesting place to hang out. They’ve put out some papers that clarify aspects of QSP (e.g., a QSP workflow) and lay out a roadmap for future activities.

So, QSP is very attractive since it has the promise of supporting mechanistic understanding of drug effects but also allowing one to capture emergent effects. However, it appears to be very problem & condition specific and it’s not clear to me how detailed I’d need to get to reach an informative model. It’s certainly not something I can pull off-the-shelf and include in my projects. But definitely worth tracking and exploring more.

Overall, it was a nice experience and quite interesting to see the current state of the art in PK/PD/QSP and learn about the challenges and successes that people are having in this area. (Also, ISoP really should make abstracts publicly linkable).