Pharmacophore modeling and QSAR analysis of anti-HBV flavonols.

Due to its global burden, Targeting Hepatitis B virus (HBV) infection in humans is crucial. Herbal medicine has long been significant, with flavonoids demonstrating promising results. Hence, the present study aimed to establish a way of identifying flavonoids with anti-HBV activities. Flavonoid structures with anti-HBV activities were retrieved. A flavonol-based pharmacophore model was established using LigandScout v4.4. Screening was performed using the PharmIt server. A QSAR equation was developed and validated with independent sets of compounds. The applicability domain (AD) was defined using Euclidean distance calculations for model validation. The best model, consisting of 57 features, was generated. High-throughput screening (HTS) using the flavonol-based model resulted in 509 unique hits. The model's accuracy was further validated using a set of FDA-approved chemicals, demonstrating a sensitivity of 71% and a specificity of 100%. Additionally, the QSAR model with two predictors, x4a and qed, exhibited predictive solid performance with an adjusted-R2 value of 0.85 and 0.90 of Q2. PCA showed essential patterns and relationships within the dataset, with the first two components explaining nearly 98% of the total variance. Current HBV therapies tend to fail to provide a complete cure, emphasizing the need for new therapies. This study's importance was to highlight flavonols as potential anti-HBV medicines, presenting a supplementary option for existing therapy. The QSAR model has been validated with two separate chemical sets, guaranteeing its reproducibility and usefulness for other flavonols by utilizing the predictive characteristics of X4A and qed. These results provide new possibilities for discovering future anti-HBV drugs by integrating modeling and experimental research.