Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.
J Mol Recognit. 2013 May;26(5):215-39. doi: 10.1002/jmr.2266.
The aim of docking is to accurately predict the structure of a ligand within the constraints of a receptor binding site and to correctly estimate the strength of binding. We discuss, in detail, methodological developments that occurred in the docking field in 2010 and 2011, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. The main developments in docking in this period, covered in this review, are receptor flexibility, solvation, fragment docking, postprocessing, docking into homology models, and docking comparisons. Several new, or at least newly invigorated, advances occurred in areas such as nonlinear scoring functions, using machine-learning approaches. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design. Where appropriate, we refer readers to exemplar case studies.
docking 的目的是在受体结合位点的约束下准确预测配体的结构,并正确估计结合强度。我们详细讨论了 2010 年和 2011 年 docking 领域的方法学发展,特别关注这一有前途的计算学科中更困难和有时有争议的方面。在这一时期,本综述涵盖了 docking 的主要发展,包括受体柔性、溶剂化、片段 docking、后处理、同源模型 docking 和 docking 比较。在非线性评分函数等领域出现了一些新的或至少新出现的进展,使用机器学习方法。本综述强烈关注药物设计背景下的 docking 进展,特别是虚拟筛选和基于片段的药物设计。在适当的情况下,我们会向读者推荐示例案例研究。