MetaCYP: a unified framework for prediction of cytochrome P450 metabolic sites and reaction types via multimodal deep learning

Cytochrome P450 (CYP) enzymes are the predominant drug-metabolizing proteins in humans, collectively governing the structural transformation of drugs and xenobiotics while directly shaping their pharmacological activity and toxicological profiles. Accurate prediction of CYP-substrate reaction sites and reaction types is therefore central to drug discovery and metabolic risk assessment. Existing computational models, however, largely depend on intrinsic molecular properties or hardcoded reaction rules, constraining their generalization across CYP isoforms.

The limitations of current models highlight the need for a more comprehensive and flexible approach to predict CYP-mediated biotransformation. In this context, MetaCYP presents a multimodal deep learning framework that predicts bonds of metabolism (BoMs) and reaction types in CYP-mediated biotransformation. This architecture encodes CYP amino acid sequences with the protein language model ESM-2 and extracts bond-level substrate features using Uni-Mol, integrating both modalities through an attention-based cross-modal fusion mechanism that captures enzyme-substrate interactions.

The proposed framework enables a single unified model to resolve isoform-specific catalytic selectivity for identical substrates. MetaCYP achieves state-of-the-art performance in BoM prediction (MCC: 0.741; ROC-AUC: 0.956) and reaction type prediction (MCC: 0.796; ROC-AUC: 0.946), outperforming current benchmarks.

The capabilities of MetaCYP position it as a practical resource for early-stage drug screening, metabolic risk assessment, and rational drug design. Its mechanistically grounded and interpretable nature offers a valuable tool for elucidating CYP catalytic selectivity and improving the accuracy of ADME property predictions. However, it is essential to note that MetaCYP is intended for research-use only and should not be used for clinical or therapeutic applications without proper validation and regulatory approval.