Machine Learning-Augmented Docking. 1. CYP inhibition prediction
Bibliographic record
Abstract
significant portion of the oxidative metabolism carried out by the human body is accomplished by six cytochrome P450 (CYP) enzymes. The binding of small molecules to these enzymes affects drug activity and half-life. Additionally, the inhibition or induction of a CYP isoform by a drug can lead to drug-drug interactions, which in turn can lead to toxicity. To predict CYP inhibition, a variety of computational methods have been used, with docking methods being less accurate than machine learning (ML) methods. However, the latter methods are sensitive to training data and show reduced accuracy on test sets outside of the chemical space represented in the training set. In contrast, docking methods do not have this generalization issue and allow for visual analysis. We hypothesize that combining ML methods with docking can improve CYP inhibition predictions. To test this hypothesis, we pair our in-house docking program FITTED with several ML techniques to investigate the accuracy and transferability of this hybrid methodology, which we term ML-augmented docking. We find that ML-augmented docking can significantly improve the accuracy of docking software while consistently surpassing the performance of ligand-only models. Additionally, we show that ML-augmented docking is more generalizable than machine learning models trained on ligand-only data. The open-source code created for this project can be found at https://github.com/MoitessierLab/ML-augmented-docking-CYP-inhibition
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".