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CYP3A4 is a major hepatic enzyme essential for metabolizing diverse chemical entities. The development of CYP3A4 substrate and inhibitor classifier models is a valuable research strategy to prevent toxicokinetics. Currently, no molecular docking model is available to classify the substrates and inhibitors for a wide range of chemical entities. This study generated a CYP3A4 substrate-inhibitor classifier model using multivariate analysis of selected variables from site of metabolism (SOM)-based docking results and molecular descriptors. CYP3A4 SOM-based molecular docking experiment was performed on the substrates and inhibitors from the DrugBank database. The relevant descriptors were selected using the area under the receiving operating curve (AUROC) and boxplot analysis. The selected variables were used to generate a CYP3A4 substrate-inhibitor classifier model using multivariate analysis. Two complexes were selected for molecular dynamic simulations. A total of 326 substrates and 154 inhibitors were successfully docked. The SOM-based docking model predicted 78.5% SOM correctly of the substrates. The CDOCKER energy, improper dihedral energy, potential energy, initial RMS (root mean square) gradient, and RMS gradient demonstrated acceptable AUROC, sensitivity, specificity, and Youden's index results. The selected variables were subjected to multivariate analysis, and the PLS-DA analysis classification model showed promising performance in predicting chemical entities' behavior as substrates or inhibitors. This model classified the external samples correctly with over 70.0% prediction accuracy. Molecular dynamic simulations showed that the selected complexes obtained from the docking model produced stable complexes. The classifier model is applicable as a research tool for the early stages of drug discovery. In silico structure-based analysis of new chemical entities toward CYP3A4 metabolism and inhibitory activity is helpful in pharmacokinetic research. This study analysed the results of SOM-based molecular docking and relevant descriptors using multivariate analysis to generate CYP3A4 substrate-inhibitor classifier model. [ABSTRACT FROM AUTHOR]

In this study, a set of 72 styryl lactone compounds reported from Goniothalamus species were docked against envelope (E), NS2B/NS3, NS5 methyltransferase (MTase), and NS5 RdRp dengue virus (DENV) protein. As a result, compounds 5, 37, 38, and 47 were identified as potential dengue protease inhibitors based on minimal docking energy values and multiple interactions with binding sites. The results from in-silico Lipinski's rule and ADMET analysis showed that these compounds were predicted to fulfil the drug-likeness properties. These ligands were found to fit in well and remain stable in the binding site of the envelope protein, NS2B/NS3, NS5 MTase, and NS5 RdRp. The results from molecular dynamic (MD) simulations indicate that the ligand–protein complex of compound 37 with NS5 MTase was stable throughout the MDs simulations and could interact with essential amino acids within the active sites. A set of 72 styryl lactone compounds reported from Goniothalamus species docked against envelope (E), NS2B/NS3, NS5 methyltransferase, and NS5 RdRp dengue virus (DENV) protein. The in-silico Lipinski's rule, ADMET, molecular docking and molecular dynamics simulation were performed in this study. Compound 37-NS5 methyltransferase complex identified as the most stable ligand-protein complex throughout the molecular dynamics simulation. [ABSTRACT FROM AUTHOR]