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

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.

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.

Extensive experiments demonstrate that HypergraphCDR consistently outperforms state-of-the-art methods in terms of Pearson Correlation Coefficient (${PCC}$), Spearman Correlation Coefficient (${SCC}$), and Coefficient of Determination (${R}^{2}$). 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.

By explicitly modeling higher-order drug-cell line relationships, HypergraphCDR substantially enhances the accuracy and robustness of cancer drug response prediction.

This study provides an effective and generalizable computational framework for drug response prediction, supporting reliable drug screening and treatment strategy development in precision medicine.