Prospective Prediction of Antitarget Activity by Matched Molecular Pairs Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Matched molecular pairs analysis (MMPA)1,2 is an inverse quantitative structure activity relationship (QSAR) technique that is rapidly gaining popularity in the retrospective analysis of large experimental datasets.3,4 While much of the recent focus has been on the differences in properties between structurally related groups of existing compounds, attempts to extend this methodology to the de-novo design of novel structures have been limited. To our knowledge the aggregate effect of multiple transformations, all suggesting the same molecular structure, has only ever being considered within a very limited dataset.5 We therefore sought to test this exciting new approach to the design (and absolute property prediction - effectively QSAR-by-MMPA) of novel chemical entities based on a larger, more diverse dataset, and couple these designs to MMPA-based predictions of antitarget activity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it