The impact of compound library size on the performance of scoring functions for structure-based virtual screening. Issue 3 (22nd June 2020)

• Record Type: Journal Article
• Authors: Fresnais, Louison; Ballester, Pedro J
• Is Part Of: Briefings in bioinformatics. Volume 22:Issue 3(2021)
• Publication Date: 2020-06-22
• Subjects: virtual screening -- big data -- machine learning -- drug design -- docking; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbaa095
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Larger 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 low-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 3-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.