{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Pang X"],"funding":["Natural Science Foundation of Beijing Municipality","National Natural Science Foundation of China"],"pagination":["34783-34792"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9086869"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["8(61)"],"pubmed_abstract":["Cytochrome P450 3A4 (CYP3A4) is an important member of the CYP family and responsible for metabolizing a broad range of drugs. Potential drug-drug interactions (DDIs) caused by CYP3A4 inhibitors could lead to increasing risk of side-effects/toxicity or decreasing effectiveness. The evaluation of CYP3A4 inhibitory activity is time-consuming, labor-intensive, and costly, and it is necessary to establish virtual screening models for predicting CYP3A4 inhibitors. In this study, 4 classifier algorithms, including support vector machine (SVM), naive Bayesian (NB), recursive partitioning (RP), and K-nearest neighbor (KNN), were applied to discriminate CYP3A4 inhibitors from the non-inhibitors. Correlation analysis and stepwise linear regression methods were used for descriptor selection and optim"],"journal":["RSC advances"],"pubmed_title":["Screening of cytochrome P450 3A4 inhibitors <i>via in silico</i> and <i>in vitro</i> approaches."],"pmcid":["PMC9086869"],"funding_grant_id":["No. 7171012","No. 81673509 No. 81573504"],"pubmed_authors":["Xiang Q","Xia J","Cui Y","Du G","Liu A","Mu G","Pang X","Zhang B","Zhao X"],"additional_accession":[]},"is_claimable":false,"name":"Screening of cytochrome P450 3A4 inhibitors <i>via in silico</i> and <i>in vitro</i> approaches.","description":"Cytochrome P450 3A4 (CYP3A4) is an important member of the CYP family and responsible for metabolizing a broad range of drugs. Potential drug-drug interactions (DDIs) caused by CYP3A4 inhibitors could lead to increasing risk of side-effects/toxicity or decreasing effectiveness. The evaluation of CYP3A4 inhibitory activity is time-consuming, labor-intensive, and costly, and it is necessary to establish virtual screening models for predicting CYP3A4 inhibitors. In this study, 4 classifier algorithms, including support vector machine (SVM), naive Bayesian (NB), recursive partitioning (RP), and K-nearest neighbor (KNN), were applied to discriminate CYP3A4 inhibitors from the non-inhibitors. Correlation analysis and stepwise linear regression methods were used for descriptor selection and optim","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Oct","modification":"2025-04-04T10:01:36.861Z","creation":"2025-04-04T10:01:36.861Z"},"accession":"S-EPMC9086869","cross_references":{"pubmed":["35547066"],"doi":["10.1039/c8ra06311g"]}}