De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry

Pikusa, M.; Rene, O.; Williams, S.; Chen, Y.-L.; Martin, E.; Godinez, W. J.; Rao, S. P. S.; Guiguemde, A. W.; Nigsch, F.

doi:10.1101/2021.12.10.472084

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bioRxiv · 10.1101/2021.12.10.472084

De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry

Pikusa, M.·Rene, O.·Williams, S.·Chen, Y.-L.·Martin, E.·Godinez, W. J.·Rao, S. P. S.·Guiguemde, A. W.·Nigsch, F.

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Abstract

Designing novel molecules with targeted biological activities and optimized physicochemical properties is a challenging endeavor in drug discovery. Recent developments in artificial intelligence have enhanced the early steps of de novo drug design and compound optimization. Herein, we present a generative adversarial network trained to design new chemical matter that satisfies a given biological signature. Our model, called pqsar2cpd, is based on the activity of compounds across multiple assays obtained via pQSAR (profile-quantitative structure-activity relationships). We applied pqsar2cpd to Chagas disease and designed a novel molecule that was experimentally confirmed to inhibit growth of parasites in vitro at low micromolar concentrations. Altogether, this approach bridges chemistry and biology into one single framework for the design of novel molecules with promising biological activity.

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Pikusa, M., Rene, O., Williams, S., Chen, Y.-L., Martin, E., Godinez, W. J., Rao, S. P. S., Guiguemde, A. W., Nigsch, F.. 2021-12-12. De-novo generation of novel phenotypically active molecules for Chagas disease from biological signatures using AI-driven generative chemistry. https://doi.org/10.1101/2021.12.10.472084

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