Improving hERG Cardiotoxicity Prediction via SMILES Transformer, Molecular Fingerprints, and Layer-Wise Self-Adaptive Graph Attention Network.

Blocking of hERG (human ether-à-go-go-related gene) potassium channels by certain drug-like molecules remains a major obstacle in pharmaceutical development, as it can induce QT interval prolongation and serious cardiotoxic effects. Experimental assessment of hERG liability is accurate but costly and time-consuming, which motivates the development of reliable computational screening tools. In this work, we propose TDMFLSGAT (transformer-enhanced deep learning model with molecular fingerprints and layer-wise self-adaptive graph attention network), a multimodal architecture that integrates three complementary molecular representations. Sequential information from SMILES strings is encoded with a Transformer, graph-based structural features are extracted using an adaptive graph attention mechanism, and physicochemical patterns are captured through a diverse set of molecular fingerprints. These modalities are fused into a unified representation for robust classification. To enhance model transparency, we further incorporate a multimodal interpretability framework that combines attention-based analyses and fingerprint-level explanations to highlight structural motifs associated with hERG blockade. Under 5-fold cross-validation, TDMFLSGAT achieves strong and well-balanced predictive performance, with an accuracy of 0.823, an AUC of 0.901, an average precision (AP) of 0.915, a sensitivity of 0.850, a specificity of 0.792, an NPV of 0.810, a PPV of 0.834, and an MCC of 0.641. The inclusion of AP confirms that these performance gains remain robust under class imbalance, providing a more realistic assessment of model reliability. Overall, these results indicate that TDMFLSGAT offers both accurate predictions and meaningful mechanistic insights, making it a promising tool for early stage cardiotoxicity screening in drug discovery.