HGACL-DRP: Heterogeneous graph attention dual-perturbation contrastive learning network for drug response prediction

The marked heterogeneity of cancer poses a substantial challenge to precision drug therapy, resulting in considerable variability in patient responses to identical treatments. Accurately predicting drug sensitivity thus represents an urgent and critical challenge in the field of pharmacology and personalized medicine. Existing methods have limitations in addressing the complexity of multi-modal biological data and heterogeneous graphs. Additionally, their heavy reliance on labeled data hinders model generalization. In this work, we propose the Heterogeneous Graph Attention Dual-Perturbation Contrastive Learning Network for Drug Response Prediction (HGACL-DRP). This framework constructs an optimized heterogeneous graph structure by integrating multi-omics features and implementing a robust neighbor filtering mechanism. HGACL-DRP innovatively introduces a Dual-Perturbation contrastive learning paradigm that incorporates both feature and structural perturbations specifically designed for drug response prediction, thereby enabling the self-supervised learning of more robust heterogeneous graph node embeddings. On two major public datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE), HGACL-DRP exhibits exceptional performance across multiple classification metrics, achieving a mean AUC of 95.48% on CCLE and 98.99% on GDSC. Compared to existing state-of-the-art drug response prediction algorithms, HGACL-DRP demonstrates statistically significant improvements in predictive accuracy. The innovative multi-view graph augmentation strategy and attention mechanism are designed to enhance personalized drug selection and facilitate novel drug discovery. HGACL-DRP is anticipated to address the challenges posed by drug response heterogeneity, thereby providing reliable support for clinical decision-making. Not applicable.