Interpretable Machine Learning for Predicting Cytochrome P450 Mechanism-Based Inhibition
Abstract
Cytochrome P450 (CYP450) enzymes play a crucial role in the metabolism of endogenous and exogenous compounds, and their inhibition can lead to clinically significant drug–drug interactions and adverse effects. Mechanism-based inhibition (MBI) is a time-dependent form of CYP450 inhibition in which enzyme-mediated bioactivation of an inhibitor can result in persistent or irreversible enzyme inactivation. Early identification of compounds with MBI liability is therefore important for reducing metabolic and drug–drug interaction risks during drug discovery. In this study, a curated dataset of 569 unique MBI compounds was compiled from the literature, while non-mechanism-based inhibitors (NMBIs) were obtained from PubChem. To construct a chemically diverse negative dataset, Butina clustering-based sampling was employed. Molecular representations based on RDKit and Mordred descriptors, MACCS keys, and ECFP fingerprints were evaluated using five machine learning algorithms, with feature selection and model optimization applied systematically. CYP-aware stratified splitting was employed to ensure appropriate representation of CYP isoform–class combinations across the training and test sets, and its performance was compared with random stratified splitting. Among the evaluated molecular representations, RDKit descriptors generally provided the strongest predictive performance, with optimized models achieving test-set ROC-AUC values of up to 0.92 in the CYP-aware evaluation. Applicability-domain analysis further demonstrated higher prediction accuracy for compounds within the defined chemical space, supporting the reliability of predictions for structurally represented compounds. In addition, structural-alert analysis identified class-associated structural motifs, including statistically significant alerts preferentially associated with MBI compounds, providing chemically interpretable information complementary to the machine learning models. Evaluation using the expanded MBI569 dataset further assessed the robustness of the predictive models across different dataset sizes and splitting strategies. Overall, this study demonstrates a systematic and interpretable approach for predicting CYP450 mechanism-based inhibition, with potential utility for the early identification of MBI liabilities during drug discovery.
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BibTeX
@misc{bhasin2026interpretable,
title = {{Interpretable Machine Learning for Predicting Cytochrome P450 Mechanism-Based Inhibition}},
author = {Khushashish Bhasin and Sudham Engle and Devendra Kumar Dhaked},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008396/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008396/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Interpretable Machine Learning for Predicting Cytochrome P450 Mechanism-Based Inhibition
AU - Khushashish Bhasin
AU - Sudham Engle
AU - Devendra Kumar Dhaked
PY - 2026
DA - 2026/09/07
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008396/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008396/v1
AB - Cytochrome P450 (CYP450) enzymes play a crucial role in the metabolism of endogenous and exogenous compounds, and their inhibition can lead to clinically significant drug–drug interactions and adverse effects. Mechanism-based inhibition (MBI) is a time-dependent form of CYP450 inhibition in which enzyme-mediated bioactivation of an inhibitor can result in persistent or irreversible enzyme inactivation. Early identification of compounds with MBI liability is therefore important for reducing metabolic and drug–drug interaction risks during drug discovery. In this study, a curated dataset of 569 unique MBI compounds was compiled from the literature, while non-mechanism-based inhibitors (NMBIs) were obtained from PubChem. To construct a chemically diverse negative dataset, Butina clustering-based sampling was employed. Molecular representations based on RDKit and Mordred descriptors, MACCS keys, and ECFP fingerprints were evaluated using five machine learning algorithms, with feature selection and model optimization applied systematically. CYP-aware stratified splitting was employed to ensure appropriate representation of CYP isoform–class combinations across the training and test sets, and its performance was compared with random stratified splitting. Among the evaluated molecular representations, RDKit descriptors generally provided the strongest predictive performance, with optimized models achieving test-set ROC-AUC values of up to 0.92 in the CYP-aware evaluation. Applicability-domain analysis further demonstrated higher prediction accuracy for compounds within the defined chemical space, supporting the reliability of predictions for structurally represented compounds. In addition, structural-alert analysis identified class-associated structural motifs, including statistically significant alerts preferentially associated with MBI compounds, providing chemically interpretable information complementary to the machine learning models. Evaluation using the expanded MBI569 dataset further assessed the robustness of the predictive models across different dataset sizes and splitting strategies. Overall, this study demonstrates a systematic and interpretable approach for predicting CYP450 mechanism-based inhibition, with potential utility for the early identification of MBI liabilities during drug discovery.
ER -