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

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

doi:10.1515/mr-2023-0030

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

Med Rev (2021). 2023 Oct 6;3(6):465-486. doi: 10.1515/mr-2023-0030. eCollection 2023 Dec.

Authors

Yanbei Li^{1

2

3}, Zhehuan Fan^{2

3}, Jingxin Rao^{2

3}, Zhiyi Chen^{1

2

3}, Qinyu Chu^{1

2

3}, Mingyue Zheng^{1

2

3}, Xutong Li^{2

3}

Affiliations

¹ School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, Zhejiang Province, China.
² Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China.
³ University of Chinese Academy of Sciences, Beijing, China.

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 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: artificial intelligence; chemogenomics; compound-protein interaction prediction; drug discovery; scoring function.

Publication types

Review