推进新药物靶点的活性化合物发现：来自人工智能驱动方法的见解，这一成果由中国科学院上海药物研究所研究团队经过不懈努力而取得。2025年6月17日出版的《中国药理学报》杂志发表了这一最新研究成果。

首先，研究组探索人工智能如何克服分子设计中的传统瓶颈，通过高精度的蛋白质结构预测和增强的对接精度实现精确的蛋白质感知。在这些以目标为中心的能力的基础上，人工智能驱动的方法还推进了配体探索，通过复杂的数据传输技术有效地连接生物和化学空间，最大限度地利用可用的活动数据。通过评估整体细胞或组织反应，人工智能在解码复杂生物系统、通过多模态数据集成驱动表型药物发现（PDD）方面发挥着关键作用。最后，研究小组揭示了人工智能如何解决与靶向以前无法治疗的蛋白质相关的挑战，例如蛋白质降解物的发展。通过综合这些前沿进展，本综述为寻求利用人工智能发现下一代治疗方法的研究人员提供了宝贵的抵抗。

据了解，发现针对新的、未开发靶点的活性化合物对于推进跨多种疾病的创新治疗至关重要。人工智能（AI）的最新进展通过显着提高传统方法所面临的效率，准确性和可扩展性，正在彻底改变活性化合物的发现。这篇综述提供了人工智能驱动的活性化合物发现方法的全面概述，特别关注它们在新靶点上的应用。

附：英文原文

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

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

Issue&Volume: 2025-06-17

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.

DOI: 10.1038/s41401-025-01591-x

Source: https://www.nature.com/articles/s41401-025-01591-x