<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Pang X</submitter><funding>Natural Science Foundation of Beijing Municipality</funding><funding>National Natural Science Foundation of China</funding><pagination>34783-34792</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9086869</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8(61)</volume><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</pubmed_abstract><journal>RSC advances</journal><pubmed_title>Screening of cytochrome P450 3A4 inhibitors &lt;i>via in silico&lt;/i> and &lt;i>in vitro&lt;/i> approaches.</pubmed_title><pmcid>PMC9086869</pmcid><funding_grant_id>No. 7171012</funding_grant_id><funding_grant_id>No. 81673509 No. 81573504</funding_grant_id><pubmed_authors>Xiang Q</pubmed_authors><pubmed_authors>Xia J</pubmed_authors><pubmed_authors>Cui Y</pubmed_authors><pubmed_authors>Du G</pubmed_authors><pubmed_authors>Liu A</pubmed_authors><pubmed_authors>Mu G</pubmed_authors><pubmed_authors>Pang X</pubmed_authors><pubmed_authors>Zhang B</pubmed_authors><pubmed_authors>Zhao X</pubmed_authors></additional><is_claimable>false</is_claimable><name>Screening of cytochrome P450 3A4 inhibitors &lt;i>via in silico&lt;/i> and &lt;i>in vitro&lt;/i> approaches.</name><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</description><dates><release>2018-01-01T00:00:00Z</release><publication>2018 Oct</publication><modification>2025-04-04T10:01:36.861Z</modification><creation>2025-04-04T10:01:36.861Z</creation></dates><accession>S-EPMC9086869</accession><cross_references><pubmed>35547066</pubmed><doi>10.1039/c8ra06311g</doi></cross_references></HashMap>