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Performance of machine-learning scoring functions in structure-based virtual screening.

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cris.lastimport.scopus2024-02-12T20:28:02Z
dc.abstract.enClassical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and −0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorSiedlecki, Paweł
dc.contributor.authorBallester, Pedro J.
dc.contributor.authorWójcikowski, Maciej
dc.date.accessioned2024-01-25T16:23:13Z
dc.date.available2024-01-25T16:23:13Z
dc.date.copyright2017-04-25
dc.date.issued2017
dc.description.accesstimeAT_PUBLICATION
dc.description.financeNie dotyczy
dc.description.numberbrak
dc.description.versionFINAL_PUBLISHED
dc.description.volume7
dc.identifier.doi10.1038/SREP46710
dc.identifier.issn2045-2322
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/115694
dc.identifier.weblinkhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5404222/
dc.languageeng
dc.pbn.affiliationbiological sciences
dc.relation.ispartofScientific Reports
dc.relation.pages1-10
dc.rightsCC-BY
dc.sciencecloudnosend
dc.subject.plbrak
dc.titlePerformance of machine-learning scoring functions in structure-based virtual screening.
dc.typeJournalArticle
dspace.entity.typePublication