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使用比较分子场分析的有序分类

Ordinal classification using Comparative Molecular Field Analysis.

作者信息

Ohgaru Takanori, Shimizu Ryo, Okamoto Kousuke, Kawase Masaya, Shirakuni Yuko, Nishikiori Rika, Takagi Tatsuya

机构信息

Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan.

出版信息

J Chem Inf Model. 2008 Jan;48(1):207-12. doi: 10.1021/ci700238k. Epub 2007 Dec 28.

Abstract

Comparative Molecular Field Analysis (CoMFA) is most widely used as one of the 3-dimensional QSAR (3D-QSAR) methods to identify the relationship between chemical structure and biological activity. Conventional CoMFA requires at least 3 orders of experimental data, such as IC50 and Ki, to obtain a good model, although practically there are many screening assays where biological activity is measured only by a rating scale. Hence, rating classification-oriented CoMFA coupled with ordinal logistic regression has been developed, and its predictive ability and 3D graphical analysis ability have been investigated. As a result, this novel CoMFA (Logistic CoMFA) has been found to be more robust than conventional CoMFAs in both predictive and 3D graphical analysis abilities. Furthermore, Logistic CoMFA is useful since it can provide the probability of each rank.

摘要

比较分子场分析(CoMFA)作为三维定量构效关系(3D-QSAR)方法之一,被最广泛地用于确定化学结构与生物活性之间的关系。传统的CoMFA需要至少3组实验数据,如IC50和Ki,才能获得一个良好的模型,尽管实际上有许多筛选试验中生物活性仅通过评分量表来衡量。因此,已经开发了面向评分分类的CoMFA与有序逻辑回归相结合的方法,并对其预测能力和三维图形分析能力进行了研究。结果发现,这种新型的CoMFA(逻辑CoMFA)在预测能力和三维图形分析能力方面都比传统的CoMFA更稳健。此外,逻辑CoMFA很有用,因为它可以提供每个等级的概率。

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