构象异构
药效团
虚拟筛选
计算机科学
稳健性(进化)
数量结构-活动关系
聚类分析
化学
算法
分子
人工智能
立体化学
机器学习
生物化学
基因
有机化学
作者
Nils-Ole Friedrich,Florian Flachsenberg,Agnes Meyder,Kai Sommer,Johannes Kirchmair,Matthias Rarey
标识
DOI:10.1021/acs.jcim.8b00704
摘要
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.
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