Half-life prediction of central nervous system (CNS) small molecules in humans using gradient tree boosting
Why this work is in the frame
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Bibliographic record
Abstract
Aims To develop a machine learning (ML) model for early-stage prediction of human half-life of oral central nervous system (CNS) drugs and to establish a curated dataset, including key in vitro and in vivo data, to support future modeling efforts.Materials & methods Human and rat half-life, plasma protein binding (PPB), and liver microsomal clearance (LM) data for 76 diverse CNS drugs and candidates were obtained from public sources or evaluated at WuXi AppTec. Gradient tree boosting (GTB) models were constructed using ChemAxon’s Trainer Engine. Feature importance was assessed, and model performance was evaluated on an external validation set.Results The best-performing model achieved 82.4% of predictions within two-fold of observed values, with a coefficient of determination (R2) of 0.75 and a root mean square error (RMSE) of 0.25. Good generalizability was confirmed using similarity-based data splitting and Y-randomization. Integration of in vitro features, preclinical in vivo data, and physicochemical properties substantially improved predictive performance. Key features driving accurate human half-life prediction were identified.Conclusion This model demonstrates practical applications for early-stage prediction of human half-life and prioritization of CNS drug candidates. The curated dataset offers a valuable resource to enhance internal databases and advance more robust predictive models.
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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 | 0.000 | 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 | 0.000 |
| 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 it