Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, BMC, Box 596, SE-751 24 Uppsala, Sweden.
Institute for Medical Engineering & Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Curr Opin Struct Biol. 2024 Aug;87:102829. doi: 10.1016/j.sbi.2024.102829. Epub 2024 Jun 6.
Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.
基于结构的虚拟筛选旨在使用复合物的计算模型来寻找与生物大分子形成有利相互作用的分子。最近，商业上可用的化学空间的激增为在数十亿种化合物中搜索治疗靶点的配体提供了机会。本文对基于结构的虚拟筛选广阔化学空间的方法进行了简要概述，重点介绍了在早期药物发现中针对治疗上重要的靶点（如 G 蛋白偶联受体和病毒酶）的成功应用。强调了探索超大化学库和与新兴机器学习技术协同作用的策略。讨论了虚拟筛选的当前机遇和未来挑战，表明该方法将在下一代药物发现管道中发挥重要作用。
