The impact of compound library size on the performance of scoring functions for structure-based virtual screening

Abstract

AbstractLarger training datasets have been shown to improve the accuracy of Machine Learning (ML)-based Scoring functions (SFs) for Structure-Based Virtual Screening (SBVS). In addition, massive test sets for SBVS, known as ultra-large compound libraries, have been demonstrated to enable the fast discovery of selective drug leads with at least nanomolar potency. This proof-of-concept was carried out on two targets using a single docking tool along with its SF. It is thus unclear whether this high level of performance would generalise to other targets, docking tools and SFs.We found that screening a larger compound library results in more potent actives being identified in all six additional targets using a different docking tool along with its classical SF. Furthermore, we established that a way to improve the potency of the retrieved molecules further is to rank them with more accurate ML-based SFs (we found this to be true in four of the six targets, the difference was not significant in the remaining two targets). A three-fold increase in average hit rate across targets was also achieved by the ML-based SFs. Lastly, we observed that classical and ML-based SFs often find different actives, which supports using both types of SFs on those targets.Contactpedro.ballester@inserm.frSupplementary informationan online-only supplementary results file is enclosed.Biographical NoteL. Fresnais carried out a master research project directly supervised by P.J Ballester and he will soon be starting a PhD.P.J Ballester has been working on virtual screening for over 15 years now. He is group leader and research scientist at cancer research centre of INSERM, the French National Institute of Health & Medical Research.