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

作者：Yanbei Li, Zhehuan Fan, Jingxin Rao, Zhiyi Chen, Qinyu Chu, Mingyue Zheng, Xutong Li · 发表于：Medical Review · 年份：2023 · DOI：10.1515/mr-2023-0030 · 被引用次数：17 · 研究领域：Computational Drug Discovery Methods、Microbial Natural Products and Biosynthesis、Bioinformatics and Genomic Networks

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 as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.