GX-936

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule "from bench to a bedside". While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure-activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.

Subtype-selective modulation of ion channels is often important, but extremely difficult to achieve for drug development. Using Nav1.7 as an example, we show that this challenge could be attributed to poor design in ion channel assays, which fail to detect most potent and selective compounds and are biased toward nonselective mechanisms. By exploiting different drug binding sites and modes of channel gating, we successfully direct a membrane potential assay toward non–pore-blocking mechanisms and identify Nav1.7-selective compounds. Our mechanistic approach to assay design addresses a significant hurdle in Nav1.7 drug discovery and is applicable to many other ion channels.