{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T18:22:16Z","timestamp":1785090136011,"version":"3.55.0"},"reference-count":90,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1955260"],"award-info":[{"award-number":["1955260"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01GM079383"],"award-info":[{"award-number":["R01GM079383"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30DA035778"],"award-info":[{"award-number":["P30DA035778"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from an SBVS. In this study, we developed a novel method, using ligand-residue interaction profiles (IPs) to construct machine learning (ML)-based prediction models, to significantly improve the screening performance in SBVSs. Such a kind of the prediction model is called an IP scoring function (IP-SF). We systematically investigated how to improve the performance of IP-SFs from many perspectives, including the sampling methods before interaction energy calculation and different ML algorithms. Using six drug targets with each having hundreds of known ligands, we conducted a critical evaluation on the developed IP-SFs. The IP-SFs employing a gradient boosting decision tree (GBDT) algorithm in conjunction with the MIN\u2009+\u2009GB simulation protocol achieved the best overall performance. Its scoring power, ranking power and screening power significantly outperformed the Glide SF. First, compared with Glide, the average values of mean absolute error and root mean square error of GBDT\/MIN\u2009+\u2009GB decreased about 38 and 36%, respectively. Second, the mean values of squared correlation coefficient and predictive index increased about 225 and 73%, respectively. Third, more encouragingly, the average value of the areas under the curve of receiver operating characteristic for six targets by GBDT, 0.87, is significantly better than that by Glide, which is only 0.71. Thus, we expected IP-SFs to have broad and promising applications in SBVSs.<\/jats:p>","DOI":"10.1093\/bib\/bbab054","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T08:19:47Z","timestamp":1612426787000},"source":"Crossref","is-referenced-by-count":32,"title":["Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction"],"prefix":"10.1093","volume":"22","author":[{"given":"Beihong","family":"Ji","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7431-7893","authenticated-orcid":false,"given":"Xibing","family":"He","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingchen","family":"Zhai","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viet Hoang","family":"Man","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9607-8229","authenticated-orcid":false,"given":"Junmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"2021090815132504000_ref1","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":"2021090815132504000_ref2","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1124\/pr.112.007336","article-title":"Computational methods in drug 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