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AutoGPA: An Automated 3D-QSAR Method Based on Pharmacophore Alignment and Grid Potential Analysis
Author(s) -
Naoyuki Asakawa,
Seiichi Kobayashi,
Jun-Ichi Goto,
N. Hirayama
Publication year - 2012
Publication title -
international journal of medicinal chemistry
Language(s) - English
Resource type - Journals
eISSN - 2090-2069
pISSN - 2090-2077
DOI - 10.1155/2012/498931
Subject(s) - pharmacophore , quantitative structure–activity relationship , computer science , grid , artificial intelligence , data mining , machine learning , chemistry , mathematics , stereochemistry , geometry
3D-QSAR approach has been widely applied and proven to be useful in the case where no reliable crystal structure of the complex between a biologically active molecule and the receptor is available. At the same time, however, it also has highlighted the sensitivity of this approach. The main requirement of the traditional 3D-QSAR method is that molecules should be correctly overlaid in what is assumed to be the bioactive conformation. Identifying an active conformation of a flexible molecule is technically difficult. It has been a bottleneck in the application of the 3D-QSAR method. We have developed a 3D-QSAR software named AutoGPA especially based on an automatic pharmacophore alignment method in order to overcome this problem which has discouraged general medicinal chemists from applying the 3D-QSAR methods to their “real-world” problems. Applications of AutoGPA to three inhibitor-receptor systems have demonstrated that without any prior information about the three-dimensional structure of the bioactive conformations AutoGPA can automatically generate reliable 3D-QSAR models. In this paper, the concept of AutoGPA and the application results will be described.

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