DOI: 10.1515/mr-2023-0030
An overview of recent advances and challenges in predicting compound-protein interaction(CPI)
Yanbei Li1
Zhehuan Fan2
Jingxin Rao2
Zhiyi Chen1
Qinyu Chu1
Mingyue Zheng1
Xutong Li3
1.School of Pharmaceutical Science and Technology,Hangzhou Institute for Advanced Study,UCAS,Hangzhou,Zhejiang Province,China;Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,Shanghai,China;University of Chinese Academy of Sciences,Beijing,China2.Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,Shanghai,China;University of Chinese Academy of Sciences,Beijing,China3.Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,555 Zuchongzhi Road,Shanghai 201203,China;University of Chinese Academy of Sciences,No.19A Yuquan Road,Beijing 100049,China
摘要：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 land-scape of CPI prediction,elucidating the challenges faced and outlining emerging trends to advance the field.
机标关键词：interactionoverviewcompoundproteinadvanceschallengespredictingrecent
论文发表日期：2023-12-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：22( 465-486 )
英文信息