hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks.

Drug-related cardiotoxicity, most notably arrhythmia, represents a major challenge in drug development. Inhibition of hERG potassium channel by certain compounds has the potential to delay cardiac repolarization, manifested as QT interval prolongation, thereby elevating the risk of severe cardiac arrhythmias like torsades de pointes (TdP). Accurate assessment of compounds' impact on hERG channels is crucial. Traditional methods are costly and inefficient for large-scale screening. Therefore, developing efficient and accurate computational methods for hERG inhibition prediction is critical. In this study, we present a deep learning framework, named hERG-MFFGNN, aimed at accurately predicting hERG channel blockers while providing model interpretability. To improve both accuracy and generalizability, we implement a multi-feature fusion strategy that systematically integrates molecular structural information. Initially, multiple molecular fingerprint features and molecular descriptors are fused to construct an initial feature representation. Then, graph neural networks are used to extract molecular topological features. These two sets of features are weighted and fused using an attention mechanism to form the final compound representation, enabling a more comprehensive expression of molecular features. The performance of hERG-MFFGNN is assessed using fivefold cross-validation on the benchmark dataset and external validation datasets. The results demonstrate that hERG-MFFGNN achieves AUROC of 0.909 and ACC of 0.854, highlighting its robust predictive capabilities for hERG activity across diverse datasets. We believe that may function as an effective instrument for the early prediction of hERG channel blockers in the phases of drug discovery and development. The complete source code is publicly accessible at https://github.com/zhaoqi106/hERG-MFFGNN .