Medical Review (Dec 2023)

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

  • Li Yanbei,
  • Fan Zhehuan,
  • Rao Jingxin,
  • Chen Zhiyi,
  • Chu Qinyu,
  • Zheng Mingyue,
  • Li Xutong

DOI
https://doi.org/10.1515/mr-2023-0030
Journal volume & issue
Vol. 3, no. 6
pp. 465 – 486

Abstract

Read online

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.

Keywords