Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions

Abstract Understanding Cytochrome P450 (CYP) enzyme-mediated metabolism is critical for accurate Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) predictions, which play a pivotal role in drug discovery. Traditional approaches, while foundational, often face challenges related to cost, scalability, and translatability. This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. Focusing on key CYP isoforms-CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4-we synthesize current research advancements and methodologies, emphasizing the integration of multi-task learning, attention mechanisms, and explainable AI (XAI) in enhancing the accuracy and interpretability of ADMET predictions. Furthermore, we address ongoing challenges, such as dataset variability and the generalization of models to novel chemical spaces. The review concludes by identifying future research opportunities, particularly in improving scalability, incorporating real-time experimental validation, and expanding focus on enzyme-specific interactions. These insights underscore the transformative potential of graph-based approaches in advancing drug development and optimizing safety evaluations.