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

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

SUBMITTER: Tao W 

PROVIDER: S-EPMC11520867 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature