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首页> 外文期刊>Bioorganic and medicinal chemistry >TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds.
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TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds.

机译:基于TOPS-MODE的QSAR,其衍生自化合物的异构系列。在设计新型抗炎化合物中的应用。

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摘要

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a [Formula: see text] -means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.
机译:使用计算机辅助分子设计在抗炎化合物中进行了拓扑亚结构分子设计(TOPS-MODE)的新应用。为了设计训练和预测集,用[式:见正文]-均值聚类分析法处理了两个系列化合物,一个包含抗炎化合物,另一个包含非抗炎化合物。开发了用于区分消炎药和非活性化合物的线性分类功能。该模型正确,清晰地将训练集中88%的活性化合物和91%的非活性化合物分类。更具体地说,该模型显示了90%的良好全局分类,即(441个案例中有399个)。在预测集中,它们对有活性和无活性化合物的总体可预测性为88%和84%,占良好分类的全球百分比为85%。此外,本文描述了片段分析,以确定一些片段对抗炎特性的作用,还分析了所选片段中卤素的存在。似乎当前基于TOPS-MODE的QSAR是抗炎发现实验的第一种替代性通用“计算机”技术。

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