An overview of recent advances and challenges in predicting compound-protein interaction (CPI)
Author:
Li Yanbei123, Fan Zhehuan23, Rao Jingxin23, Chen Zhiyi123, Chu Qinyu123, Zheng Mingyue123, Li Xutong23ORCID
Affiliation:
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
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.
Funder
China Postdoctoral Science Foundation Shanghai Municipal Science and Technology Major Project National Natural Science Foundation of China National Key Research and Development Program of China SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program Lingang Laboratory
Publisher
Walter de Gruyter GmbH
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