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Molecules 2016, 21(1), 1; doi:10.3390/molecules21010001

Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds

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
3
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
*
Author to whom correspondence should be addressed.
Academic Editor: Peter Willett
Received: 29 October 2015 / Revised: 9 December 2015 / Accepted: 15 December 2015 / Published: 23 December 2015
(This article belongs to the Special Issue Chemoinformatics)
View Full-Text   |   Download PDF [633 KB, uploaded 23 December 2015]   |  

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
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Salmina, E.S.; Haider, N.; Tetko, I.V. Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds. Molecules 2016, 21, 1.

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