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Seoulpowergrid, as Viktors mentioned, this paper doesn't directly relate to Dr Perryman's work on World Community Grid, although the techniques he describes could certainly be useful to several World Community Grid projects. While we're always excited to share our researcher partners' work, our News section is intended for sharing updates on progress made as a result of research conducted on World Community Grid. This is why we opted to promote this paper through other communication channels (such as the forums) instead.

Hi Juan and all the volunteers,

Your certainty was well-founded. Our new machine learning model for predicting metabolic stability (i.e., the Mouse Liver Microsome stability Bayesian) will be used as one way to help filter the docking results from OpenZika. After using docking-based filters on the millions of compounds from the ZINC libraries, I plan to use this metabolic stability filter to further narrow down the results, to find promising compounds that should have a better chance of working "in vivo."

Best wishes,
Dr. Alex L. Perryman

Dear KLiK,

The details regarding the GO Fight Against Malaria experiments that have been analyzed thus far (including the # of compounds analyzed, tested, and how well they performed during in vitro experiments) were described in Project Updates that I previously wrote. See:

https://www.worldcommunitygrid.org/about_us/viewNewsArticle.do?articleId=373
and
https://www.worldcommunitygrid.org/about_us/viewNewsArticle.do?articleId=433

When I was still part of the FightAIDS@Home team, I wrote many Newsletters and gave a couple webinars that described similar details. Those are linked to the FightAIDSatHome.scripps.edu site. Since I left Professor Art Olson's lab at TSRI in Oct. of 2013, and joined Professor Joel Freundlich's lab at Rutgers University-New Jersey Medical School in Nov. of 2013, I am no longer "in the loop" regarding the # of FAAH compounds that have been tested and what results and discoveries have been produced in the last few years.

GO FAM results have not yet led to a cure for malaria, but we have only been able to analyze and experimentally test a very small % of the GO FAM predictions. We have submitted a few different grants to try to obtain funding to purchase compounds and get them tested in "wet lab" experiments, as well as to synthesize analogs of hits and get them tested, so that we can try to optimize the initial hits. But those grants were not approved. Funding rates from the NIH are still at historic lows (i.e., less than 10% of grant applications get funded for this type of research). We are still trying to get funding to enable testing more of the GO FAM results. I will post updates when new discoveries have been made. But I cannot describe the exact details of ongoing projects until they have been published (or else I would not be able to publish them).

It is important to note that the GO FAM team did not "wet lab" collaborators as part of the effort when we started that project. Conversely, there are already several "wet lab" Zika experts who are part of the OpenZika team, which will enable us to test the OpenZika predictions more rapidly.

The Mouse Liver Microsomal stability Bayesian has not yet been combined with GO FAM docking results (or other types of docking results). We made and validated that stability model very recently. But it should be a useful tool to combine with GO FAM and with OpenZika docking results, in order to help filter the compounds and focus on the small set that are more likely to be active against the pathogen and also be metabolically stable in mice. Mouse studies of malaria and Zika (and many other diseases) are an important hurdle that must be crossed when optimizing initial hits and when translating the advanced (optimized) compounds into the pre-clinical phase of development. Metabolic stability in mice is a key metric or property that potential therapeutic compounds must have (if the mouse liver degrades a compound quickly, then the compound will never reach the bug). Consequently, I will try combing our new stability model with the OpenZika results. If that new workflow is successful, we will let you all know in project updates and in future publications.

Thank you for your interest and your support!

Best wishes,
Dr. Alex L. Perryman