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An overview of recent advances and challenges in predicting compound-protein interaction(CPI) 被引量:1
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作者 Yanbei Li Zhehuan Fan +4 位作者 Jingxin Rao Zhiyi Chen Qinyu Chu Mingyue Zheng Xutong Li 《Medical Review》 2023年第6期465-486,共22页
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 ... 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. 展开更多
关键词 compound-protein interaction prediction drug discovery artificial intelligence scoring function chemogenomics
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