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• W3035777330 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." @default.
• W3035777330 created "2020-06-25" @default.
• W3035777330 creator A5005597587 @default.
• W3035777330 creator A5042374222 @default.
• W3035777330 date "2020-06-22" @default.
• W3035777330 modified "2023-10-14" @default.
• W3035777330 title "The impact of compound library size on the performance of scoring functions for structure-based virtual screening" @default.
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• W3035777330 doi "https://doi.org/10.1093/bib/bbaa095" @default.
• W3035777330 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32568385" @default.
• W3035777330 hasPublicationYear "2020" @default.
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