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In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.<\/jats:p>","DOI":"10.1093\/bib\/bbz173","type":"journal-article","created":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T12:11:51Z","timestamp":1579781511000},"page":"497-514","source":"Crossref","is-referenced-by-count":66,"title":["Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions"],"prefix":"10.1093","volume":"22","author":[{"given":"Chao","family":"Shen","sequence":"first","affiliation":[]},{"given":"Ye","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xujun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Haiyang","family":"Zhong","sequence":"additional","affiliation":[]},{"given":"Gaoang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaojun","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Dongsheng","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2020,1,25]]},"reference":[{"key":"2021012203431903000_ref1","doi-asserted-by":"crossref","first-page":"7874","DOI":"10.1021\/jm5006463","article-title":"Polypharmacology: challenges and opportunities in drug discovery","volume":"57","author":"Anighoro","year":"2014","journal-title":"J Med Chem"},{"key":"2021012203431903000_ref2","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1021\/ar800236t","article-title":"Efficient drug lead discovery and optimization","volume":"42","author":"Jorgensen","year":"2009","journal-title":"Acc Chem Res"},{"key":"2021012203431903000_ref3","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1038\/nrd1549","article-title":"Docking and scoring in virtual screening for drug discovery: methods and applications","volume":"3","author":"Kitchen","year":"2004","journal-title":"Nat Rev Drug Discov"},{"key":"2021012203431903000_ref4","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.3389\/fphar.2018.01089","article-title":"Empirical scoring functions for structure-based virtual screening: applications, critical aspects and Challenges","volume":"9","author":"Guedes","year":"2018","journal-title":"Front Pharmacol"},{"key":"2021012203431903000_ref5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s12551-016-0247-1","article-title":"Software for molecular docking: a review","volume":"9","author":"Pagadala","year":"2017","journal-title":"Biophys Rev"},{"key":"2021012203431903000_ref6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1023\/A:1011115820450","article-title":"DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases","volume":"15","author":"Ewing","year":"2001","journal-title":"J Comput Aided Mol Des"},{"key":"2021012203431903000_ref7","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1006\/jmbi.1996.0897","article-title":"Development and validation of a genetic algorithm for flexible docking","volume":"267","author":"Jones","year":"1997","journal-title":"J Mol Biol"},{"key":"2021012203431903000_ref8","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/S1093-3263(02)00164-X","article-title":"LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites","volume":"21","author":"Venkatachalam","year":"2003","journal-title":"J Mol Graph Model"},{"key":"2021012203431903000_ref9","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1002\/jcc.21334","article-title":"Update AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and Multithreading","volume":"31","author":"Trott","year":"2010","journal-title":"J Comput Chem"},{"key":"2021012203431903000_ref10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1023\/A:1016357811882","article-title":"Further development and validation of empirical scoring functions for structure-based binding affinity prediction","volume":"16","author":"Wang","year":"2002","journal-title":"J Comput Aided Mol Des"},{"key":"2021012203431903000_ref11","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1021\/jm0306430","article-title":"Glide: a new approach for rapid, accurate docking and scoring. 1. 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I. 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