A combination of pharmacophore modeling, molecular docking, and virtual screening for P2Y<sub>12</sub> receptor antagonists from Chinese herbs
Bibliographic record
Abstract
P2Y12, a member of the G-protein-coupled receptors, is associated with abnormal platelet aggregation, a condition that contributes to thrombus formation. As receptor antagonists are effective solutions for anti-thrombus, the P2Y12 receptor is a popular drug target. After the recent resolution of the P2Y12 receptor’s crystal structure, pharmacophore modeling and docking were combined to discover potential natural antagonists. Various approaches were used for the validation of the pharmacophore models and the optimization of docking algorithms. Hypo18, which was generated by 24 known antagonists, was determined to be the best hypothesis and is comprised of one ring aromatic, one hydrogen bond acceptor, one exclude volume, and three hydrophobic features. Hypo18 was thus utilized to screen TCMD (version 2009) to identify any potential active compounds, which then resulted in a hit list of 121 compounds with drug-likeness analysis. In addition, docking was used to refine the pharmacophore-based screening results as a cross-linking method. Then, the top 20 compounds with high docking scores were reserved. This paper provides a reliable source for discovering natural P2Y12 receptor antagonists from traditional Chinese herbs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".