Compound protein interaction (CPI) prediction is an important task in lead discovery and drug repositioning. Although in vitro assays are reliable, their high cost and low throughput limit their application in large-scale screening. Recent deep learning methods have provided efficient computational alternatives for CPI prediction; however, their generalizability in realistic virtual screening settings remains limited, particularly under cold-start scenarios involving unseen compounds or proteins.
Here, we present MoCL-CPI, a multimodal framework for CPI prediction. MoCL-CPI integrates protein sequences, molecular structures, physicochemical descriptors, multiple sequence alignment features, and embeddings derived from a large-scale heterogeneous biomedical network to represent compounds and proteins from complementary structural, functional, topological, and evolutionary perspectives. In particular, the biomedical network-based modality captures global associations and functional context that are not directly available from local structural representations, thereby improving model generalization in challenging screening settings.
We evaluated MoCL-CPI on three public benchmark datasets, including C. elegans, BindingDB, and BioSNAP. Experimental results show that MoCL-CPI achieves improved robustness and predictive performance across diverse screening scenarios. Notably, under inductive data splits that more closely resemble cold-start settings, the proposed framework shows more consistent advantages, suggesting that multimodal collaborative learning provides more stable support for interaction prediction involving unseen entities. These results suggest that multimodal representation learning can be an effective strategy for mitigating the cold-start problem in CPI prediction and may provide a useful framework for computational compound screening.

Co-authors: Jiao Li