A year in publications – 2014

A year in collaborative publications, the ups and downs and a few random comments as well (with a big thanks to all involved):

1. Ekins S and Freundlich JS and Coffee M, A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus, F1000research, 3: 277, 2014.

This came initially from a Twitter exchange of papers containing FDA drugs. Pretty speculative. Initially was part of a much bigger paper (which is a story in itself). Several other ideas came at around the same time and hopefully they will see the light of day.

2. Ekins S, Collecting rare diseases, F1000research, 3, 260, 2014.

I was asked by F1000Research to put a collection together. This highlights some of the difficulties patients have in getting their ideas and work published.

3. Litterman NK, Rhee M, Swinney DC and Ekins S, Collaboration for rare disease drug discovery research, F1000research, 3:261, 2014.

This is the result of a good collaboration from 4 diverse backgrounds, I connected to one co-author via Twitter.

4. Dong Z, Ekins S and Polli JE, A substrate pharmacophore for the human sodium taurocholate co-transporting polypeptide, 478(1):88-95, 2014.

This manuscript came together pretty quickly in 2014, I think it’s the first such paper on NTCP substrates.

5. Lipinski CA, Litterman N, Southan C, Williams AJ, Clark AM and Ekins S. Parallel worlds of public and commercial bioactive chemistry data, J Med Chem, In Press 2014.

This project started from a discussion and was recently covered in detail here.

6. Litterman N, Lipinski CA, Bunin BA and Ekins S, Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes, J Chem Inf Model, 54:2996-3004, 2014.

This project started from a discussion and was recently covered in detail here and here.

7. Litterman N, and Ekins S, Databases and collaboration require standards for human stem cell research, Drug Disc Today, In press 2014.

This was initially an idea from discussion with the editor of Nature Genetics. It was rejected by that Journal. We also tried several other journals. I think it’s a great proposal / idea and could be achieved very readily. The challenge is how to get groups on board.

8. Ekins S, Freundlich JS and Reynolds RC, Are bigger datasets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis, J Chem Inf Model, 54:2157-65, 2014.

Possibly the logical extension of the TB machine learning papers. Combining all datasets from the SRI/NIAID work.

9. Ekins S, Hacking into the granuloma: could antibody antibiotic conjugates be developed for TB? Tuberculosis, 94(6):715-6, 2014.

This came from a discussion over dinner when I was asked for a crazy idea. I then pulled together the basis of the commentary. It’s a pretty simple idea, building on whats been done for cancer but as far as I can tell never tried for TB. Next step is to actually do it.

10. Ekins S, Clark AM, Swamidass SJ, Litterman N and Williams AJ, Bigger data, collaborative tools and the future of predictive drug discovery, J Comp-Aided Mol Design, 54:2157-65, 2014.

An invited review for the journal, took a good amount of effort to put this together, pulling different ideas into a cohesive document. I like the end result.

11. Ekins S, Nuermberger EL and Freundlich JS, Minding the gaps in Tuberculosis research, Drug Disc Today, 19:1279-82, 2014.

This brief commentary takes the JCIM paper below and expands it. We tried Science Translational Medicine (rejected after review), Trends in Microbiology (triaged at proposal stage),

12. Sames L, Moore A, Arnold RJG and Ekins S, Recommendations to enable drug development for inherited neuropathies: Charcot-Marie-Tooth and Giant Axonal Neuropathy, F1000Research, 3:83, 2014.

This paper came out of the work we put into writing a RDCRN grant proposal in 2013 which we are still mining for additional grant proposals. A great collaboration with Parent/ patient advocates. This also marked our first submission to F1000Research.

13. Clark AM, Sarker M and Ekins S, New target predictions and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0, J Cheminform 6: 38, 2014.

This paper really highlights the incredible work of Alex Clark. How we took the update for the mobile app and added models, made descriptors open source and more.

14. Ekins S and Perlstein EO, Ten simple rules of live tweeting at scientific conferences, PLOS Comp Biol, 10(8):e1003789, 2014.

This little editorial was the surprise of the year for me and I have discussed its formation previously. An idea we had walking from a conference on our way to dinner. It took a while for this paper to get published.

15. Ekins S, Pottorf R, Reynolds RC, Williams AJ, Clark AM, Freundlich JS, Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis, J Chem Inf Model, 54:1070-82, 2014.

All the work for this paper was performed in 2013. We tried J Med Chem first before JCIM.

16. Dong Z, Ekins S and Polli JE, Quantitative NTCP pharmacophore and lack of association between DILI and NTCP inhibition, Eur J Pharm Sci, 66:1-9, 2014.

A paper that was written based on work from 2013. We had to try a few journals before this one made it out there.

17. Krasowski MD and Ekins S, Using cheminformatics to predict cross reactivity of “designer drugs” to their currently available immunoassays. J Cheminform 6:22, 2014.

A paper written early this year from work Matt Krasowski and I did in 2013, more investigation of Bath salts and similarity to immunoassays.

18. Krasowski MD, Drees D, Morris CS, Maakestad J, Blau JL and Ekins S, Cross-reactivity of Steroid Hormone Immunoassays: Clinical Significance and Two-Dimensional Molecular Similarity Prediction, BMC Clinical Pathology, BMC Clin Pathol, 14:33, 2014.

A paper written in 2013 from work done in 2012 with Matt Krasowski, looking at steroids and immunoassays cross reactivity.

19. Godbole AA, Ahmed W, Bhat RS, Bradley EK, Ekins S and Nagaraja V, Inhibition of Mycobacterium tuberculosis I by m-AMSA, a eukaryotic type II topoisomerase poison. Biochem Biophys Res Comm, 446:916-20, 2014.

Written from 2012-2013, a collaboration with a group in India as part of the MM4TB project. The first of 2 papers using docking for this target.

20. Ekins S and Williams AJ, Curing TB with open science, Tuberculosis, 94:183-5, 2014.

Written with Tony in 2013, from a discussion we had one day over coffee..what if there was more open science for TB?

21. Kandel BA, Ekins S, Leuner K, Thasler WE, Harteneck C and Zanger UM, No activation of human PXR by hyperforin-related phloroglucinols, JPET, 348:393-400, 2014.

Written in 2013, a collaboration with a German group, I generated all the PXR model predictions. One of the few examples of a “negative data” paper being published that I have been involved with!

22. Ekins S, Casey A.C, Roberts D, Parish T. and Bunin BA, Bayesian Models for Screening and TB Mobile for Target Inference with Mycobacterium tuberculosis, Tuberculosis, 94:162-9, 2014.

Written in 2013, as the third external evaluation of TB Bayesian models published to date.

23. Ekins S, Freundlich JS, Hobrath JV, White EL, Reynolds RC, Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery, Pharm Res, 31: 414-35, 2014.

Written in 2013, using data from the SRI ARRA grant which made a very useful test set for the various TB machine learning models.

24. Ponder EL, Freundlich JS, Sarker M, Ekins S, Computational models for neglected diseases: gaps and opportunities, Pharm Res, 31: 271-277, 2014.

This was written in 2013 primarily using data collected for a grant proposal. It’s a very brief summary of where computers have been used for these diseases too.

25. Ekins S, Progress in computational toxicology, J Pharmacol Toxicol Methods, 69:115-140 2014.

This was written in 2013 initially as a book chapter, the editor wanted to change it dramatically and I did not so opted to turn into a review.

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