Psoralen and Bergapten: In Silico Metabolism and Toxicophoric Analysis of Drugs Used to Treat Vitiligo
Bibliographic record
Abstract
PURPOSE: To discuss the contribution of psoralen and bergapten metabolites on psoralens toxicity. METHODS: Computational chemistry prediction of metabolic reactions and toxicophoric groups based on the expert systems Derek and Meteor. RESULTS: a total of 15 metabolites were suggested for both psoralen and bergapten based on phase 1 and 2 biotransformations until the 3rd generation. Five toxicophoric substructures were shared among psoralen, bergapten and their corresponding metabolites; one toxicophoric marker (resorcinol) was only identified in bergapten and its biotransformation products. CONCLUSION: Although the toxic effects of psoralens are well known and documented, there is little information concerning the role of their metabolites in this process. We believe this work add to the knowledge of which molecular substructures are relevant to the process of metabolism and toxicity induction, thus guiding the search and development of more effective and less toxic drugs to treat vitiligo.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".