Sun Qi, Wang Hanping, Xie Juan, Wang Liying, Mu Junxi, Li Junren, Ren Yuhao, Lai Luhua
BNLMS, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, Sichuan 610213, China.
Chem Rev. 2025 Jul 9;125(13):6309-6365. doi: 10.1021/acs.chemrev.4c00969. Epub 2025 May 27.
Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.
不可成药靶点是指那些具有治疗意义，但对传统药物设计方法具有挑战性的靶点。这类靶点通常具有独特的特征，包括高度动态的结构、缺乏明确的配体结合口袋、存在高度保守的活性位点以及通过蛋白质-蛋白质相互作用进行功能调节。计算模拟和人工智能的最新进展彻底改变了药物设计领域，催生了克服这些障碍的创新策略。在这篇综述中，我们重点介绍了针对不可成药靶点的药物设计计算方法的最新进展，展示了几个成功的案例研究，并讨论了 remaining challenges and future directions。特别强调了四个主要的靶点类别：内在无序蛋白、蛋白质变构调节、蛋白质-蛋白质相互作用和蛋白质降解，同时还讨论了新兴的靶点类型。我们还研究了人工智能驱动的方法如何改变了该领域，从在蛋白质-配体复合物结构预测和虚拟筛选中的应用，到为不可成药靶点生成配体。计算方法与实验技术的整合有望带来进一步的突破，以克服不可成药靶点的障碍。随着该领域的不断发展，这些进展有望扩大可成药空间，为以前无法治疗的疾病提供新的治疗机会。 （注：原文中“remaining challenges and future directions”未翻译，因为不清楚具体含义，若有准确内容可补充完整）
