In silico design of a dual TPR/TxS inhibitor for venous thromboembolism and related cardiovascular diseases
Bibliographic record
Abstract
In recent years, many research efforts have been directed towards preventing vasoconstrictor and platelet aggregatory properties of TxA2 related to the prostaglandin cycle, as TxA2 has been implicated directly or indirectly in pathologies such as cardiovascular diseases, venous thromboembolism, and pulmonary embolism. The TxA2 antagonists and TxS inhibitors undergoing clinical trials have not shown the expected clinical efficacy. This molecular modeling and docking study explains how efficacy may be enhanced by a careful design of multitarget drugs producing synergistic effects simultaneously at different targets. A dual TPR/TxS inhibition strategy is expected to give better clinical efficacy. This study also emphasizes the importance of designing efforts based on detailed analysis of drug−receptor interactions at both targets. Ab initio HF/6-31G(d) and B3LYP/6-31G(d) molecular orbital calculations coupled with flexible ligand docking studies have led to the design of a dual TPR/TxS inhibitor starting from a naturally occurring compound bromelain, derived from pineapple extract with some known pharmacological advantages. A designed lead compound may prove to be a fruitful starting point for the development of clinically efficient drugs for venous thromboembolism and related cardiovascular diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".