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构象生成器:构象集合的一种新方法。

Conformator: A Novel Method for the Generation of Conformer Ensembles.

机构信息

Center for Bioinformatics , Universität Hamburg , Bundesstrasse 43 , 20146 Hamburg , Germany.

Department of Chemistry , University of Bergen , N-5020 Bergen , Norway.

出版信息

J Chem Inf Model. 2019 Feb 25;59(2):731-742. doi: 10.1021/acs.jcim.8b00704. Epub 2019 Feb 12.

Abstract

Computer-aided drug design methods such as docking, pharmacophore searching, 3D database searching, and the creation of 3D-QSAR models need conformational ensembles to handle the flexibility of small molecules. Here, we present Conformator, an accurate and effective knowledge-based algorithm for generating conformer ensembles. With 99.9% of all test molecules processed, Conformator stands out by its robustness with respect to input formats, molecular geometries, and the handling of macrocycles. With an extended set of rules for sampling torsion angles, a novel algorithm for macrocycle conformer generation, and a new clustering algorithm for the assembly of conformer ensembles, Conformator reaches a median minimum root-mean-square deviation (measured between protein-bound ligand conformations and ensembles of a maximum of 250 conformers) of 0.47 Å with no significant difference to the highest-ranked commercial algorithm OMEGA and significantly higher accuracy than seven free algorithms, including the RDKit DG algorithm. Conformator is freely available for noncommercial use and academic research.

摘要

计算机辅助药物设计方法,如对接、药效团搜索、3D 数据库搜索和 3D-QSAR 模型的创建,都需要构象集合来处理小分子的柔性。在这里,我们提出了 Conformator,这是一种用于生成构象集合的准确有效的基于知识的算法。在处理所有测试分子的 99.9%方面,Conformator 因其对输入格式、分子几何形状和大环处理的稳健性而脱颖而出。通过扩展用于采样扭转角的规则集、用于大环构象生成的新算法以及用于组装构象集合的新聚类算法,Conformator 达到了中位数最小均方根偏差(在与蛋白质结合的配体构象和最多 250 个构象的集合之间测量)为 0.47 Å,与排名最高的商业算法 OMEGA 没有显著差异,并且比包括 RDKit DG 算法在内的七个免费算法的准确性更高。Conformator 可免费用于非商业用途和学术研究。

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