Bridging chemical structure and conceptual knowledge enables accurate prediction of compound-protein interaction

Abstract

Abstract Background Accurate prediction of compound-protein interaction (CPI) plays a crucial role in drug discovery. Existing data-driven methods aim to learn from the chemical structures of compounds and proteins yet ignore the conceptual knowledge that is the interrelationships among the fundamental elements in the biomedical knowledge graph (KG). Knowledge graphs provide a comprehensive view of entities and relationships beyond individual compounds and proteins. They encompass a wealth of information like pathways, diseases, and biological processes, offering a richer context for CPI prediction. This contextual information can be used to identify indirect interactions, infer potential relationships, and improve prediction accuracy. In real-world applications, the prevalence of knowledge-missing compounds and proteins is a critical barrier for injecting knowledge into data-driven models. Results Here, we propose BEACON, a data and knowledge dual-driven framework that bridges chemical structure and conceptual knowledge for CPI prediction. The proposed BEACON learns the consistent representations by maximizing the mutual information between chemical structure and conceptual knowledge and predicts the missing representations by minimizing their conditional entropy. BEACON achieves state-of-the-art performance on multiple datasets compared to competing methods, notably with 5.1% and 6.6% performance gain on the BIOSNAP and DrugBank datasets, respectively. Moreover, BEACON is the only approach capable of effectively predicting knowledge representations for knowledge-lacking compounds and proteins. Conclusions Overall, our work provides a general approach for directly injecting conceptual knowledge to enhance the performance of CPI prediction.

Keywords

• Compound-protein interactions
• Knowledge graphs
• Incomplete multi-modal representation learning
• Self-supervised learning