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Advancing active compound discovery for novel drug targets: insights from AI-driven approaches

Acta Pharmacologica Sinica (2025)Cite this article

Abstract

The discovery of active compounds for novel, underexplored targets is essential for advancing innovative therapeutics across a wide range of diseases. Recent advancements in artificial intelligence (AI) are revolutionizing active compound discovery by dramatically enhancing the efficiency, accuracy, and scalability previously challenged by traditional methods. This review provides a comprehensive overview of AI-driven methodologies for active compound discovery, with a particular focus on their application to novel targets. Initially, we explore how AI overcomes traditional bottlenecks in molecular design, enabling precise protein perception through high-accuracy protein structure prediction and enhanced docking precision. Building upon these target-focused capabilities, AI-driven approaches also advance ligand exploration, effectively bridging biological and chemical spaces through sophisticated data transfer techniques that maximize the utility of available activity data. By assessing overall cellular or organismal responses, AI plays a pivotal role in decoding complex biological systems, driving phenotypic drug discovery (PDD) through multi-modal data integration. Finally, we discuss how AI is addressing challenges associated with targeting previously undruggable proteins, exemplified by the development of protein degraders. By synthesizing these cutting-edge advancements, this review serves as a valuable resource for researchers seeking to leverage AI in the discovery of next-generation therapeutics.

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Acknowledgements

This work was supported by the following grants: the Strategic Priority Research Program of the Chinese Academy of sciences (XDB0830000), National Natural Science Foundation of China (82204278, T2225002 and 82273855), SIMM-SHUTCM Traditional Chinese Medicine Innovation Joint Research Program (E2G805H), Shanghai Municipal Science and Technology Major Project, National Key Research and Development Program of China (2023YFC2305904 and 2022YFC3400504), Key Technologies R&D Program of Guangdong Province (2023B1111030004), Shanghai Sailing Program (24YF2755600), and the China Postdoctoral Science Foundation (2024M763421).

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  1. These authors contributed equally: Xing-you Wang, Yang Chen

Authors and Affiliations

  1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China

    Xing-you Wang, Yang Chen, Yu-fan Li, Chao-yang Wei, Meng-ya Liu, Chen-xing Yuan, Yao-yu Zheng, Mo-han Qin, Yu-feng Sheng, Xiao-chu Tong, Ming-yue Zheng & Xu-tong Li

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xing-you Wang, Yang Chen, Yu-fan Li, Chao-yang Wei, Meng-ya Liu, Chen-xing Yuan, Yao-yu Zheng, Mo-han Qin, Yu-feng Sheng, Xiao-chu Tong, Ming-yue Zheng & Xu-tong Li

  3. School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou, 330106, China

    Yang Chen, Meng-ya Liu, Chen-xing Yuan, Yao-yu Zheng, Mo-han Qin, Yu-feng Sheng & Ming-yue Zheng

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Contributions

XYW: Writing—Original draft. YChen: Writing—Original draft. YFL: Writing—Original Draft. CYW: Writing—Original Draft. MYL: Literature Search and Screening. CXY: Writing—Original Draft. YYZ: Writing—Original Draft. MHQ: Writing—Original Draft. YFS: Writing—Original Draft. XCT: Conceptualization and Supervision. MYZ: Conceptualization and Supervision. XTL: Writing—Review and Editing, Conceptualization, and Supervision.

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Correspondence to Ming-yue Zheng or Xu-tong Li.

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Wang, Xy., Chen, Y., Li, Yf. et al. Advancing active compound discovery for novel drug targets: insights from AI-driven approaches. Acta Pharmacol Sin (2025). https://doi.org/10.1038/s41401-025-01591-x

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