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Due to dengue fever's swift global expansion and the lack of effective antiviral remedies, it is crucial to discover and develop new antiviral drugs. The study aimed to assess the antiviral potential of the extract and fractions from the leaves of Goniothalamus lanceolatus Miq. (Annonaceae). New Guinea C strain DENV-2, at a multiplicity of infection of 0.4, was used to infect Vero cells. The assessment of antiviral effectiveness was conducted through the plaque reduction assay. Deep metabolome analysis of the active fraction identified nine styryllactones based on reference standards and eighteen styryllactones were further annotated through a molecular database search. One new 2 H -tetrahydropyran derivative, 3- epi -goniothalesdiol A (19), together with seven known styryllactones (8 , 11 , 14 , 16 , 17 , 18 , and 22) were isolated from the leaves of G. lanceolatus. The active bis-styryllactone goniolanceolatin A (22) with the highest selectivity index (SI) underwent further evaluation using quantitative reverse transcription qRT-PCR to determine the viral RNA level. The qRT-PCR data showed that the IC 50 value for the active compound was 5.07 µg/mL, and its corresponding SI value was 5.30. Following comprehensive docking studies, the bis-styryllactone derivative 22 showed potential binding interactions with crucial amino acids of the Envelope (E) of DENV proteins comparable to those of ribavirin. [Display omitted] • In vitro evaluation of Goniothalamus lanceolatus leaf dichloromethane (GLLD) extract showed the strongest anti-dengue activity. • One new 2H-tetrahydropyran derivative was successfully isolated from the leaves of G. lanceolatus during a bioactivity-directed. • In vitro and in silico evaluation of bis-styryl lactone isolated from the most active fraction of leaves of G. lanceolatus showed the most potential anti-dengue activity. [ABSTRACT FROM AUTHOR]

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]