A QSAR analysis to explain the analgesic properties of <i>Aconitum</i> alkaloids
Bibliographic record
Abstract
Aconitum roots are traditionally prescribed for the management of different types of painful affections in Asiatic countries. A quantitative structure-activity relationship (QSAR) analysis was performed to study the effect of chemical substitutes in the analgesic potency of alkaloids available in Chinese Aconitum roots. Using the CAChe program package for biomolecules, molecular modelling was performed in 12 alkaloids previously tested in a model of acetic acid-induced writhing in rats. The ED50 (micromol/kg) was used as the activity parameter. Structural parameters were compared between alkaloids with an aroyl/aroyloxy group at R14 and alkaloids with the aroyloxy group at R4. Single linear regression analyses were performed in order to find the parameters explaining activity. Alkaloids with an aroyl/aroyloxy group at R14 exhibited the highest potency (significantly less ED50). The stability parameters were different between groups, e.g. total energy was -8.0 +/- 0.4 in the potent analgesic alkaloids and -6.7 +/- 0.3 in the weak analgesic alkaloids (P = 0.001). The reactivity index of C2', C3' and C5' of the aromatic ring was also different between groups, e.g. the reactivity index of C5' was 40.8 +/- 0.6 in potent analgesic alkaloids and 48.1 +/- 0.6 in weaker analgesic alkaloids (P < 0.001). Several structural parameters explained analgesic activity of alkaloids, being the reactivity index of C5' on the aromatic group the most important factor (r = 0.89; P < 0.001).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".