Development of Quantitative Structure−Activity Relationships and Classification Models for Anticonvulsant Activity of Hydantoin Analogues
Bibliographic record
Abstract
Classification and QSAR analysis was performed on a large set of hydantoin derivatives with measured anticonvulsant activity in mice and rats. The classification set comprised 287 hydantoins having maximal electroshock (MES) activity expressed in qualitative form. A subset of 94 hydantoins with MES ED(50) values was used for QSAR analysis. Numerical descriptors were generated to encode topological, geometric/structural, electronic, and thermodynamic properties of molecules. Analyses were performed with training and test sets of diverse compounds selected using their representation in a principal component space. Cell- and distance metric-based selection methods were employed in this process. For QSAR, a genetic algorithm (GA) was used for selecting subsets of 5-9 descriptors that minimize the rms error on the training sets. The most predictive models have rms errors of 0.86 (r(2) = 0.64) and 0.73 (r(2) = 0.75) ln(1/ED(50)) units on the cell- and distance metric-derived test sets, respectively, and showed convergence in the selected descriptors. Classification models were developed using recursive partitioning (RP) and spline-fitting with a GA (SFGA), a novel method we have implemented. The most predictive RP and SFGA models have classification rates of 75% and 80% on the test sets; both methods produced models with similar discriminating features. For QSAR and classification, consensus schemes gave improved predictive accuracy.
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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 | 0.001 | 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 | 0.003 |
| 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".