GLMCyp: A Deep Learning-Based Method for CYP450-Mediated Reaction Site Prediction.

Abstract

Cytochrome P450 enzymes (CYP450s) play crucial roles in metabolizing many drugs, and thus, local chemical structure can profoundly influence drug efficacy and toxicity. Therefore, the accurate prediction of CYP450-mediated reaction sites can increase the efficiency of drug discovery and development. Here, we present GLMCyp, a deep learning-based approach, for predicting CYP450 reaction sites on small molecules. By integrating two-dimensional (2D) molecular graph features, three-dimensional (3D) features from Uni-Mol, and relevant CYP450 protein features generated by ESM-2, GLMCyp could accurately predict bonds of metabolism (BoMs) targeted by a panel of nine human CYP450s. Incorporating protein features allowed GLMCyp application in broader CYP450 metabolism prediction tasks. Additionally, substrate molecular feature processing enhanced the accuracy and interpretability of the predictions. The model was trained on the EBoMD data set and reached an area under the receiver operating characteristic curve (ROC-AUC) of 0.926. GLMCyp also showed a relatively strong capacity for feature extraction and generalizability in validation with external data sets. The GLMCyp model and data sets are available for public use (https://github.com/lvimmind/GLMCyp-Predictor) to facilitate drug metabolism screening.