Ligand-based pharmacophore modeling, virtual screening, and 2D quantitative structure-activity relationship performance on anti-Hepatitis B virus flavonols
Bibliographic record
Abstract
Background: Targeting Hepatitis B virus (HBV) infection in human is crucial due to its adverse. Herbal medicine has long been significant in this regard, with flavonoids demonstrating promising results. Hence, establishing a way of identifying flavonoids with anti-HBV activities is the aim of the present study. Methods: 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. Applicability domain (AD) was defined using Euclidean distance calculations for model validation. Results: The best model, consisting of 57 features was generated. HTS using the flavonol-based model resulted in 509 unique hits. The model's sensitivity and specificity were 71% and 100%, respectively. The QSAR model with two predictors, x4a and qed, exhibited strong predictive performance with an adjusted-R² value of 0.85 and 0.90 of Q2. Conclusion: The QSAR model has been validated with two separate chemical sets, guaranteeing the model's 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".