文献类型：期刊文献

英文题名：Target fishing: from "needle in haystack" to "precise guidance"--new technology, new strategy and new opportunity

作者：Chen, Juan[1];Guo, Yafei[2];Shao, Jing[1];Guo, Mei[1];Zhu, Xinyu[1]

第一作者：陈杰;陈军;陈静

通信作者：Guo, M[1];Zhu, XY[1]

机构：[1]Gansu Univ Chinese Med, Coll Pharm, Lanzhou 730000, Gansu, Peoples R China;[2]Capital Med Univ, Sch Tradit Chinese Med, Beijing, Peoples R China

第一机构：甘肃中医药大学药学院（西北中藏药协同创新中心办公室）

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Coll Pharm, Lanzhou 730000, Gansu, Peoples R China.|[1073501e14fb35863569f]甘肃中医药大学药学院（西北中藏药协同创新中心办公室）;[10735]甘肃中医药大学;

年份：2025

卷号：16

外文期刊名：FRONTIERS IN PHARMACOLOGY

收录：;Scopus(收录号：2-s2.0-105022718299);WOS:【SCI-EXPANDED(收录号:WOS:001619439100001)】；

基金：The author(s) declare that financial support was received for the research and/or publication of this article. The authors acknowledge the following funding sources for supporting this study: Joint Funds of the National Natural Science Foundation of China (U21A20412); The Postdoctoral Fellowship Program of CPSF under Grant Number (GZC20231758.); Gansu Provincial Department of Education University Teachers Innovation Fund Project (2024A-091); Beijing Postdoctoral Science Foundation (2024-198); Gansu Province genuine medicinal materials quality standardization technology research and promotion engineering laboratory open fund project (ddyc-2022-04).

语种：英文

外文关键词：machine learning; artificial intelligence; target fishing; nature products; drug discovery

摘要：Drug target discovery is the core breakthrough point of new drug research and development. The chemical complexity and biological network regulation characteristics of natural product systems with a long history of clinical application pose a challenge to the traditional single-target research paradigm. Although traditional technologies based on molecular docking and chemical probes are still dominant, breakthroughs in disruptive technologies such as artificial intelligence and deep learning are driving the transformation of research methods from 'broad-spectrum screening' to 'precise capture'. This review systematically discusses the latest progress of drug target capture technology. Studies have shown that the deep integration of deep learning and knowledge graph not only significantly improves the accuracy of target prediction, but also constructs an interdisciplinary collaboration network across chemical informatics, systems biology and clinical medicine. The fusion of this technology shows three core advantages: multi-dimensional drug-target interaction analysis ability based on deep representation learning; integrate the dynamic predictive modeling ability of multi-omics data; and the interpretable decision support ability with clinical transformability. The purpose of this paper is to provide a theoretical framework for the academic community, and to build a bridge from basic research to clinical application, so as to promote the development of precision drugs into a new era of intelligent drive.