Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction

被引：103

作者：

Ferrari, T. ^{[1
]}

Cattaneo, D. ^{[1
]}

Gini, G. ^{[1
]}

Bakhtyari, N. Golbamaki ^{[2
]}

Manganaro, A. ^{[2
]}

Benfenati, E. ^{[2
]}

机构：

[1] Politecn Milan, Dept Elect & Informat, I-20133 Milan, Italy

[2] Ist Ric Farmacol Mario Negri, Dept Environm Hlth Sci, Milan, Italy

来源：

SAR AND QSAR IN ENVIRONMENTAL RESEARCH | 2013年 / 24卷 / 05期

关键词：

SARpy; structural alerts; mutagenicity; data mining; SMILES; QSAR; CARCINOGENICITY; SALMONELLA; MODELS; TOXICITY; ALERTS; VECTOR; SYSTEM; SMILES; DEREK;

D O I：

10.1080/1062936X.2013.773376

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

This work proposes a new structure-activity relationship (SAR) approach to mine molecular fragments that act as structural alerts for biological activity. The entire process is designed to fit with human reasoning, not only to make the predictions more reliable but also to permit clear control by the user in order to meet customized requirements. This approach has been tested on the mutagenicity endpoint, showing marked prediction skills and, more interestingly, bringing to the surface much of the knowledge already collected in the literature as well as new evidence.

引用

页码：631 / 649

页数：19

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