Spline‐Fitting with a Genetic Algorithm: A Method for Developing Classification Structure—Activity Relationships.
Bibliographic record
Abstract
Classification methods allow for the development of structure−activity relationship models when the target\nproperty is categorical rather than continuous. We describe a classification method which fits descriptor\nsplines to activities, with descriptors selected using a genetic algorithm. This method, which we identify as\nSFGA, is compared to the well-established techniques of recursive partitioning (RP) and soft independent\nmodeling by class analogy (SIMCA) using five series of compounds: cyclooxygenase-2 (COX-2) inhibitors,\nbenzodiazepine receptor (BZR) ligands, estrogen receptor (ER) ligands, dihydrofolate reductase (DHFR)\ninhibitors, and monoamine oxidase (MAO) inhibitors. Only 1-D and 2-D descriptors were used. Approximately\n40% of compounds in each series were assigned to a test set, “cherry-picked” from the complete set such\nthat they lie outside the training set as much as possible. SFGA produced models that were more predictive\nfor all but the DHFR set, for which SIMCA was most predictive. RP gave the least predictive models for\nall but the MAO set. A similar trend was observed when using training and test sets to which compounds\nwere randomly assigned and when gradually eliminating compounds from the (designed) training set. The\nstability of models was examined for the random and reduced sets, where stability means that classification\nstatistics and the selected descriptors are similar for models derived from different sets. Here, SIMCA produced\nthe most stable models, followed by SFGA and RP. We show that a consensus approach that combines all\nthree methods outperforms the single best model for all data sets.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".