Structure‐Based Prediction of QT Prolongation Risk Using Graph Neural Networks: An Integrative Approach Combining In Vitro hERG Assays and Pharmacovigilance Data

Drug‐induced QT‐interval prolongation, a non‐specific biomarker of increased risk for Torsades de Pointes (TdP), is a major safety concern in drug development. While in vitro hERG inhibition assays are required for early‐phase screening, pharmacovigilance data from sources like the FDA Adverse Event Reporting System (FAERS) provide complementary insights. Integrating these heterogeneous data with molecular structure offers a promising, yet underutilized approach to predict proarrhythmic risk. We developed an interpretable graph neural network (GNN) framework integrating in vitro hERG inhibition data (PubChem AID 588834), FAERS‐derived pharmacovigilance signals, and molecular structure information. Canonical SMILES were converted into molecular graphs using RDKit, and atom‐ and bond‐level features were encoded. Four GNN architectures (GINE, GCN, GraphSAGE, and GATv2) were compared via stratified five‐fold cross‐validation. The best‐performing model, GATv2, was further interpreted using Integrated Gradients to identify structural features contributing to QT liability. The final data set comprised 4,808 small molecules with binary QT‐risk labels. GATv2 achieved a cross‐validated ROC‐AUC of 0.838, PR‐AUC of 0.830, and F1‐score of 0.756. Retraining on the full data set yielded ROC‐AUC 0.918, PR‐AUC 0.908, and F1‐score 0.847. External validation on an independent hERG assay (AID 1671200, n = 2,405) confirmed strong performance (ROC‐AUC 0.859, sensitivity 0.80, specificity 0.82). Atomic degree and hydrogen count were dominant predictors, consistent with known SARs. This GNN‐based framework integrates structural and pharmacological data to predict QT risk, providing a transparent, structure‐based decision‐support tool aligned with ICH S7B/E14 and CiPA guidelines.