An overview of recent advances and challenges in predicting compound-protein interaction (CPI).

Abstract

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs.Machine learning (ML) algorithms have emerged aspowerful toolsforCPI prediction, offering notable advantages in cost-effectiveness and efficiency. This reviewprovides an overview of recent advances in both structure-based andnon-structure-based CPI prediction ML models, highlighting their performance and achievements. It alsooffers insights into CPI prediction-related datasets andevaluation benchmarks. Lastly, the article presents acomprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.