自动生长：一种用于蛋白质抑制剂设计的新算法。
AutoGrow: a novel algorithm for protein inhibitor design.
作者信息
Durrant Jacob D, Amaro Rommie E, McCammon J Andrew
机构信息
Biomedical Sciences Program, University of California, San Diego, La Jolla, CA 92093-0365, USA.
出版信息
Chem Biol Drug Des. 2009 Feb;73(2):168-78. doi: 10.1111/j.1747-0285.2008.00761.x.
Abstract
Due in part to the increasing availability of crystallographic protein structures as well as rapid improvements in computing power, the past few decades have seen an explosion in the field of computer-based rational drug design. Several algorithms have been developed to identify or generate potential ligands in silico by optimizing the ligand-receptor hydrogen bond, electrostatic, and hydrophobic interactions. We here present AutoGrow, a novel computer-aided drug design algorithm that combines the strengths of both fragment-based growing and docking algorithms. To validate AutoGrow, we recreate three crystallographically resolved ligands from their constituent fragments.
摘要
部分由于晶体学蛋白质结构的可得性不断增加以及计算能力的迅速提高，在过去几十年中，基于计算机的合理药物设计领域出现了爆发式增长。已经开发了几种算法，通过优化配体 - 受体氢键、静电和疏水相互作用来在计算机上识别或生成潜在配体。我们在此介绍AutoGrow，这是一种新颖的计算机辅助药物设计算法，它结合了基于片段生长算法和对接算法的优势。为了验证AutoGrow，我们从其组成片段重新创建了三种晶体学解析的配体。
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