Quantification of contributions of different molecular fragments for antioxidant activity of coumarin derivatives based on QSAR analyses
Bibliographic record
Abstract
Attempts have been made in the present work using in silico techniques for identification of essential structural features imparting antioxidant potential to naturally available coumarin molecules and their synthetic derivatives. Four different types of modeling tools have been employed for the qualitative and quantitative assessment of the molecular fragments constituting the biological pharmacophore. The descriptor-based quantitative structure–activity relationship (QSAR) and group-based QSAR (G-QSAR) models provide a quantitative estimation of the substituent requirements and the chemical nature of the parent moiety. Subsequently, 3D pharmacophore and hologram QSAR (HQSAR) models enable identification of the key molecular components necessary for the antioxidant potency to the molecules. All of the different models infer the importance of the hydrogen bond acceptor ketonic fragment for interaction of the antioxidant molecules with the neighbouring toxic radicals. Additionally, the phenyl substituent attached to the side chain and the benzene nucleus of the benzopyran moiety also constitute the response pharmacophore for the molecules under study. The models thus developed may serve as an essential query tool for screening of databases for selection of molecules bearing the essential fragments and subsequent prediction of their free radical scavenging potency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".