QSAR based analysis of fatal drug induced renal toxicity
Bibliographic record
Abstract
This study is aimed at finding Quantitative Structure Activity Relationships (QSAR) for drugs reported to result in fatal consequence due to kidney failure and categorized as Adverse Drug Reactions (ADR). Study is based on the reports from open source Canada Vigilance Adverse Reaction Online database. Biological toxicity of small molecules has been predicted as a function of molecular structural features represented by their molecular descriptors. QSAR methods used have identified the structural features of the drugs/molecules and predicted their toxicity. Drugs suspected to cause kidney failure as ADR were analyzed. The molecular descriptors of these drugs were obtained using DRAGON web interface. The structural characteristics that distinguish drugs reported to cause death due to kidney failure as ADR against drugs not causing death but causing kidney failure as ADR were checked. Three QSAR methods used to find the relationships were Simple Kmeans clustering, decision tree and linear regression analysis. The greater value of the descriptor MAXDP is favorable for preventing death has been illustrated by all three models. The 9-membered ring of the benzimidazole substructure can be inferred from Pubchem database to contribute positively towards death. The descriptor, T(N..P), sum of topological distances between N..P 2D atom pairs, would prevent death if its value is lowered. A decrease in value of the descriptor, PCR - ratio of multiple path count over path count, will result in a decrease in probability of fatal consequences. The study predicts that renal toxicity could be decreased if the above mentioned molecular descriptors are modified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".