MetExPred: A Comprehensive Prediction Framework with Protein-Context-Aware Multi-view Learning for Drug Metabolism and Excretion
- 1Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen
Abstract
Within the ADMET continuum, metabolism and excretion (ME) form a critical bridge between drug exposure established by absorption and distribution and downstream efficacy and toxicity. However, no existing framework for drug ME prediction has simultaneously achieved broad coverage of endpoints and robust data recency and completeness. Here, we developed MetExPred, a multi-view prediction framework that comprises 17 classification endpoints and two regression endpoints, covering the overall drug ME process. The framework combines sequence-based and graph-based molecular representations, while optionally incorporating ESM-2 protein representations when experimentally annotated targets are available. A protein-aware masking strategy enables the same architecture to operate in both molecular-only and target-enhanced settings, and multi-view attention adaptively integrates the available representations. Across the classification tasks, MetExPred achieved mean AUROC, AUPRC and F1 scores of 0.844, 0.734 and 0.697, respectively. For clearance and half-life prediction, the model achieved RMSE values of 0.686 and 0.668. MetExPred showed the strongest average performance among the evaluated baselines, while ablation studies confirmed the complementary contributions of molecular sequence, graph and protein information. These results provide a unified and flexible modeling framework for systematic ME prediction of drug compounds, enabling early-stage virtual screening and pharmacokinetic assessment during lead optimization.
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BibTeX
@misc{huang2026metexpred,
title = {{MetExPred: A Comprehensive Prediction Framework with Protein-Context-Aware Multi-view Learning for Drug Metabolism and Excretion}},
author = {Y. Huang and J. Wan and G. Wu and D. Dong and Y.-C.-D. Lin and H.-Y. Huang and H.-D. Huang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.21.753060},
url = {https://www.biorxiv.org/content/10.64898/2026.09.21.753060v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - MetExPred: A Comprehensive Prediction Framework with Protein-Context-Aware Multi-view Learning for Drug Metabolism and Excretion
AU - Y. Huang
AU - J. Wan
AU - G. Wu
AU - D. Dong
AU - Y.-C.-D. Lin
AU - H.-Y. Huang
AU - H.-D. Huang
PY - 2026
DA - 2026/09/21
PB - bioRxiv
DO - 10.64898/2026.09.21.753060
UR - https://www.biorxiv.org/content/10.64898/2026.09.21.753060v1
AB - Within the ADMET continuum, metabolism and excretion (ME) form a critical bridge between drug exposure established by absorption and distribution and downstream efficacy and toxicity. However, no existing framework for drug ME prediction has simultaneously achieved broad coverage of endpoints and robust data recency and completeness. Here, we developed MetExPred, a multi-view prediction framework that comprises 17 classification endpoints and two regression endpoints, covering the overall drug ME process. The framework combines sequence-based and graph-based molecular representations, while optionally incorporating ESM-2 protein representations when experimentally annotated targets are available. A protein-aware masking strategy enables the same architecture to operate in both molecular-only and target-enhanced settings, and multi-view attention adaptively integrates the available representations. Across the classification tasks, MetExPred achieved mean AUROC, AUPRC and F1 scores of 0.844, 0.734 and 0.697, respectively. For clearance and half-life prediction, the model achieved RMSE values of 0.686 and 0.668. MetExPred showed the strongest average performance among the evaluated baselines, while ablation studies confirmed the complementary contributions of molecular sequence, graph and protein information. These results provide a unified and flexible modeling framework for systematic ME prediction of drug compounds, enabling early-stage virtual screening and pharmacokinetic assessment during lead optimization.
ER -