A dataset of 67 berberine derivatives for the inhibition of butyrylcholinesterase (BuChE) was studied based on the combination of quantitative structure–activity relationships models, molecular docking, and molecular dynamics methods. First, a series of berberine derivatives were reported, and their inhibitory activities toward butyrylcholinesterase (BuChE) were evaluated. By 2D- quantitative structure–activity relationships studies, the best model built by partial least-square had a conventional correlation coefficient of the training set (R²) of 0.883, a cross-validation correlation coefficient () of 0.777, and a conventional correlation coefficient of the test set () of 0.775. The model was also confirmed by Y-randomization examination. In addition, the molecular docking and molecular dynamics simulation were performed to better elucidate the inhibitory mechanism of three typical berberine derivatives (berberine, C2, and C55) toward BuChE. The predicted binding free energy results were consistent with the experimental data and showed that the van der Waals energy term (ΔE_vdw) difference played the most important role in differentiating the activity among the three inhibitors (berberine, C2, and C55). The developed quantitative structure–activity relationships models provide details on the fine relationship linking structure and activity and offer clues for structural modifications, and the molecular simulation helps to understand the inhibitory mechanism of the three typical inhibitors. In conclusion, the results of this study provide useful clues for new drug design and discovery of BuChE inhibitors from berberine derivatives.

The anti-influenza virus activities of 50 resveratrol (RV: 3, 5, 4′-trihydroxy-trans-stilbene) derivatives were evaluated using a neuraminidase (NA) activity assay. The results showed that 35 compounds exerted an inhibitory effect on the NA activity of the influenza virus strain A/PR/8/34 (H1N1) with 50% inhibitory concentration (IC₅₀) values ranging from 3.56 to 186.1 μm. Next, the 35 RV derivatives were used to develop 3D quantitative structure–activity relationship (3D QSAR) models for understanding the chemical–biological interactions governing their activities against NA. The comparative molecular field analysis (CoMFA r² = 0.973, q² = 0.620, q_test² = 0.661) and the comparative molecular similarity indices analysis (CoMSIA r² = 0.956, q² = 0.610, q_test² = 0.531) were applied. Afterward, molecular docking was performed to study the molecular interactions between the RV derivatives and NA. Finally, a cytopathic effect (CPE) reduction assay was used to evaluate the antiviral effects of the RV derivatives in vitro. Time-of-addition studies demonstrated that the RV derivatives might have a direct effect on viral particle infectivity. Our results indicate that the RV derivatives are potentially useful antiviral compounds for new drug design and development for influenza treatment.

The flavonoid baicalein has been proven effective in animal models of parkinson's disease; however, the potential biological targets and molecular mechanisms underlying the antiparkinsonian action of baicalein have not been fully clarified. In the present study, the potential targets of baicalein were predicted by in silico target fishing approaches including database mining, molecular docking, structure-based pharmacophore searching, and chemical similarity searching. A consensus scoring formula has been developed and validated to objectively rank the targets. The top two ranked targets catechol-O-methyltransferase (COMT) and monoamine oxidase B (MAO-B) have been proposed as targets of baicalein by literatures. The third-ranked one (N-methyl-d-aspartic acid receptor, NMDAR) with relatively low consensus score was further experimentally tested. Although our results suggested that baicalein significantly attenuated NMDA-induced neurotoxicity including cell death, intracellular nitric oxide (NO) and reactive oxygen species (ROS) generation, extracellular NO reduction in human SH-SY5Y neuroblastoma cells, baicalein exhibited no inhibitory effect on [³H]MK-801 binding study, indicating that NMDAR might not be the target of baicalein. In conclusion, the results indicate that in silico target fishing is an effective method for drug target discovery, and the protective role of baicalein against NMDA-induced neurotoxicity supports our previous research that baicalein possesses antiparkinsonian activity.