Prediction of Cancer Drug Response Based on Hypergraph Convolutional Network and Contrastive Learning.

OBJECTIVE: Accurate prediction of cancer drug responses is essential for advancing precision oncology. This work aims to improve generalization and robustness in drug response prediction by modeling complex drug-cell line interactions. METHODS: We propose HypergraphCDR, a Hypergraph Convolutional Network model with Hypergraph Contrastive Learning for cancer drug response prediction. Multiomics features of cancer cell lines are first compressed using an autoencoder. A Hypergraph is then constructed to capture high-order relationships between drugs and cancer cell lines, with Hypergraph Convolutional Networks generating drug embeddings. In parallel, cell line embeddings are generated via a neural network. Drug and cell line embeddings are jointly optimized and integrated into a regression framework using a combination of supervised regression loss and contrastive loss. RESULTS: Extensive experiments demonstrate that HypergraphCDR consistently outperforms state-of-the-art methods in terms of Pearson Correlation Coefficient ($\mathit {PCC}$), Spearman Correlation Coefficient ($\mathit {SCC}$), and Coefficient of Determination ($\mathit {R}^{2}$). Moreover, in independent experiments involving unseen drugs and unseen cell lines, as well as tissue-specific evaluations, HypergraphCDR exhibits superior generalization performance compared with recent baselines. CONCLUSION: By explicitly modeling higher-order drug-cell line relationships, HypergraphCDR substantially enhances the accuracy and robustness of cancer drug response prediction. SIGNIFICANCE: This study provides an effective and generalizable computational framework for drug response prediction, supporting reliable drug screening and treatment strategy development in precision medicine.