Abstract
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.