Conformator: A Novel Method for the Generation of Conformer Ensembles
Conformator: A Novel Method for the Generation of Conformer Ensembles
复制标题
DOI：
10.1021/acs.jcim.8b00704
复制
发表时间：
2019-02-01
影响因子：
5.6
通讯作者：
Rarey, Matthias
中科院分区：
文献类型：
--
作者：
Friedrich, Nils-Ole, V;Flachsenberg, Florian;Rarey, Matthias
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，需要构象集合来处理小分子的灵活性。在这里，我们提出了Conformator，一种准确有效的基于知识的构象集成生成算法。在处理了99.9%的测试分子后，Conformator凭借其在输入格式、分子几何和大环处理方面的健壮性而脱颖而出。通过扩展的扭转角采样规则集、大环构象生成的新算法和构象集合组装的新聚类算法，Conformator达到了0.47埃的中值最小均方根偏差(在蛋白质结合的配体构象和最大250个构象的系综之间测量)，与排名最高的商业算法omega没有显著差异，但显著高于包括RDKit DG算法在内的七个自由算法。Conformator可免费用于非商业用途和学术研究。
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 angstrom 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.