Multitask Deep Learning for CYP Inhibition, Inactivation, and Metabolic Soft-Spot Prediction Download PDF

CYP enzymes dominate drug clearance, and early recognition of inhibition, inactivation, and metabolic soft spots is central to modern drug design. A unified computational view of these liabilities could help prioritize safer chemical series before costly downstream testing. Existing models often treat reversible inhibition, time-dependent inactivation, and site-of-metabolism prediction as separate tasks. This separation can obscure the shared chemical determinants that drive binding, bioactivation, and metabolic transformation. This article describes a multitask deep learning model that jointly predicts CYP reversible inhibition, time-dependent inactivation, and metabolic soft-spot location from molecular structure. The objective is to use a shared molecular representation to support more consistent and data-efficient metabolic profiling. The proposed model uses a graph neural network backbone shared across three prediction heads. These heads conceptually support isoform-specific inhibition prediction, TDI risk prediction, and atom-level soft-spot localization within the same molecular framework. Conceptually, the model would be expected to improve consistency across related metabolic endpoints compared with isolated single-task systems. It could also connect molecule-level liability predictions with atom-level explanations that guide medicinal chemistry interpretation. A unified metabolic profiling model could streamline CYP liability assessment in early discovery. By combining inhibition, inactivation, and soft-spot prediction, such a model could provide a comprehensive and interpretable metabolic hazard panel from a single molecular input.