Robust Strategy for Hit-to-Lead Discovery: NMR for SAR
Bibliographic record
Abstract
Establishing robust structure-activity relationships (SARs) is key to successful drug discovery campaigns, yet it often remains elusive due to screening and hit validation artifacts (false positives and false negatives), which frequently result in unproductive downstream expenditures of time and resources. To address this issue, we developed an integrative biophysics-driven strategy that expedites hit-to-lead discovery, mitigates false positives/negatives and common hit validation errors, and provides a robust approach to obtaining accurate binding and affinity measurements. The advantage of this method is that it vastly improves the clarity and reproducibility for affinity-driven SAR by monitoring and eliminating confounding factors. We demonstrate the ease at which high-quality micromolar binders can be generated from the initial millimolar fragment screening hits against an "undruggable" protein target, HRas.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 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".