Compared to monotherapy, drug combinations exhibit stronger efficacy, fewer side effects, and lower drug resistance in cancer treatment. However, traditional wet-lab methods for screening synergistic drug combinations are both costly and inefficient. Lately, the development of various drug synergy methods has been promoted by the emergence of multiple drug synergy databases. Many of these methods use multimodal data and achieve good results. However, if various modalities of data is given equal consideration without taking into account the differences in features between the two modalities, this may lead to less effective multi-modal learning. We propose a multi-modal contrastive learning method for drug synergy prediction, named MCDSP. Specifically, MCDSP extracts entity embedding features of drugs and cell lines from heterogeneous graphs, while leveraging molecular fingerprints and gene expression features as biomolecular features for drugs and cell lines. These two different types of features serve as two types of modality information. Under the guided of single modality prediction tasks, we evaluated the relevant information of each modality. Through contrastive learning, the prediction bias of the two modalities are reduced, which obtain improved quality of multi-modal feature. Experiments show that MCDSP outperforms baseline methods on large datasets, and it performs well in handling unknown drug combinations and cell lines. MCDSP has demonstrated significant effectiveness in predicting drug synergy. This study aligns the features of two modalities towards the effective components through contrastive learning, thereby improving the quality of multimodal features and significantly improving the performance of drug synergy prediction models. We innovatively utilized contrastive learning guided by single modality prediction tasks, which sets this study apart from previous research and provides a new tool for drug synergy prediction.
Luo et al. (2025) studied this question.