Prediction of Adverse Drug Reactions to Anticancer Agents Based on Heterogeneous Graph Neural Networks

Multi-source data on drugs, proteins, diseases, and adverse drug reactions (ADRs) were integrated from DrugBank, STRING, CTD, and SIDER to construct a heterogeneous graph. A model named MP-HGNN based on short meta-path aggregation was proposed. It decomposes long meta-paths into semantically clear two-hop segments for local information fusion, aggregates them via learnable channel weights, and outputs drug-ADR association risks as conditional probabilities.

Results Experimental results demonstrated that the MP-HGNN model achieved an area under the curve (AUC) of 0.9684 and an area under the precision-recall curve (AUPR) of 0.8386, outperforming 13 baseline models in both discriminative ability and calibration. Ablation studies confirmed the effectiveness of the short-path design and feature fusion strategy. The model enables interpretable backtracking of predictions through short evidence chains (e.g., drug-protein-disease-ADR).

Literature verification confirmed that high-confidence predictions were consistent with established pharmacological mechanisms and clinical reports. Conclusions The "data harmonization and short-path aggregation" paradigm proposed in this study effectively mitigates semantic drift in long-path analysis. It balances prediction performance with interpretability and transferability, offering a practical technical solution for intelligent early warning and pre-medication risk assessment of anticancer drug adverse reactions.