MetaboGNN: predicting liver metabolic stability with graph neural networks and cross-species data

The metabolic stability of a drug is a crucial determinant of its pharmacokinetic properties, including clearance, half-life, and oral bioavailability. Accurate predictions of metabolic stability can significantly streamline the drug discovery process. In this study, we present MetaboGNN, an advanced model for predicting liver metabolic stability based on Graph Neural Networks (GNNs) and Graph Contrastive Learning (GCL). Using a high-quality dataset from the 2023 South Korea Data Challenge for Drug Discovery, which comprises 3,498 training molecules and 483 test molecules, we presented molecular structures as graphs to capture the intricate structural relationships that influence metabolic stability. A GCL-driven pretraining step was employed to enhance model generalizability by learning robust, transferable graph-level representations. Notably, incorporating interspecies differences between human liver microsomes (HLM) and mouse liver microsomes (MLM) further improved predictive accuracy, achieving Root Mean Square Error (RMSE) values of 27.91 (HLM) and 27.86 (MLM), both expressed as the percentage of parent compound remaining after a 30-min incubation. Compared to traditional approaches, MetaboGNN demonstrates superior predictive performance and highlights the importance of considering interspecies enzymatic variations. In addition, attention-based analysis identified key molecular fragments associated with metabolic stability, highlighting chemically meaningful structural determinants. These findings establish MetaboGNN as a powerful tool for metabolic stability prediction, supporting more efficient lead optimization processes in drug discovery. This study presents MetaboGNN, a novel predictive framework for liver metabolic stability that integrates Graph Neural Networks with Graph Contrastive Learning. By explicitly incorporating interspecies metabolic differences as a dedicated learning target, MetaboGNN achieved enhanced prediction accuracy. Attention-based analysis also identified molecular fragments that are strongly associated with stabilizing or destabilizing effects, facilitating chemical insights during lead optimization.