Momentum Contrast-Enhanced Multimodal Representation Learning for Drug Synergy Prediction

MOTIVATION: Accurate prediction of synergistic drug combinations can accelerate anticancer combination discovery. Existing methods inadequately model higher-order drug-drug-cell-line interactions and drug-disease associations and remain sensitive to sparse and noisy multi-omics data, limiting generalization to unseen cell lines and drug combinations. RESULTS: We present MoCo-MultiSynergy, a multimodal framework that combines modality-specific momentum contrastive learning with heterogeneous hypergraph modeling. The hypergraph represents synergistic drug-drug-cell-line triplets and drug-disease associations, while gated residual propagation refines node representations. Momentum Contrast modules regularize encoded drug and cell-line representations using latent feature masking and Gaussian perturbation. On the O'Neil and NCI-ALMANAC datasets, MoCo-MultiSynergy achieves the highest AUROC and AUPRC across the evaluated settings, with the largest gains when generalizing to unseen cell lines and drug combinations. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/27167199/MoCo-MultiSynergy.