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首页> 外文期刊>Acta Crystallographica, Section B. Structural Science >Conformational Analysis from Crystallographic Data using Conceptual Clustering
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Conformational Analysis from Crystallographic Data using Conceptual Clustering

机译：使用概念聚类从晶体学数据进行构象分析

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The rapid growth of crystallographic databases has created a demand for novel and efficient techniques for the analysis of molecular conformations, in order to derive new concepts and rules and to generate useful classifications of the available data. This paper presents a conceptual clustering approach, termed IMEM (image memory), which discovers the conformational diversity present in a dataset of crystal structures. In contrast to numerical clustering methods, IMEM views a molecular structure as comprising qualitative relationships among its parts, i.e. the structure is viewed as a molecular scene. In addition, IMEM does not require the user to have any a priori knowledge of an expected number of conformational classes within a given dataset. The IMEM approach is applied to several datasets derived from the Cambridge Structural Database and, in all cases, chemically correct and sensible conformational classifications were discovered. This is confirmed by a rigorous comparison of IMEM results with published conformational data obtained by energy-minimization and numerical clustering methods. Conformational analysis tools have an important part to play in the conversion of raw molecular databases to knowledge bases.
机译：晶体学数据库的快速增长产生了对用于分子构象分析的新颖和有效技术的需求，以便得出新的概念和规则并生成可用数据的有用分类。本文提出了一种称为IMEM（图像内存）的概念性聚类方法，该方法发现了晶体结构数据集中存在的构象多样性。与数值聚类方法相反，IMEM认为分子结构在其各个部分之间包括定性关系，即该结构被视为分子场景。另外，IMEM不需要用户对给定数据集中的构象类别的预期数量有任何先验知识。 IMEM方法应用于从剑桥结构数据库衍生的几个数据集，并且在所有情况下，都发现了化学正确和合理的构象分类。通过将IMEM结果与通过能量最小化和数值聚类方法获得的已公布构象数据进行严格比较，可以证实这一点。构象分析工具在将原始分子数据库转换为知识库方面起着重要作用。

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