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1

Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, Freiberg D-09596, Germany

2

Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, Vienna A-1090, Austria

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Institute of Structural Biology, Helmholtz Zentrum München—Research Center for Environmental Health GmbH, Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany

4

BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany





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Academic Editor: Peter Willett

Abstract The article describes a classification system termed -extended functional groups- EFG, which are an extension of a set previously used by the CheckMol software, that covers in addition heterocyclic compound classes and periodic table groups. The functional groups are defined as SMARTS patterns and are available as part of the ToxAlerts tool http:-ochem.eu-alerts of the On-line CHEmical database and Modeling OCHEM environment platform. The article describes the motivation and the main ideas behind this extension and demonstrates that EFG can be efficiently used to develop and interpret structure-activity relationship models. View Full-Text

Keywords: chemical functional groups; heterocyclic compounds; chemoinformatics analysis; machine learning; data interpretation chemical functional groups; heterocyclic compounds; chemoinformatics analysis; machine learning; data interpretation





Autor: Elena S. Salmina 1, Norbert Haider 2 and Igor V. Tetko 3,4,*

Fuente: http://mdpi.com/



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