title: Extended Functional Groups (EFG): An Efficient Set for Chemical Characterization and Structure-Activity Relationship Studies of Chemical Compounds.
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.
authors.affiliation: Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, Freiberg D-09596, Germany. salmina@mailserver.tu-freiberg.de.
authors.forename: Elena S
authors.initials: ES
authors.lastname: Salmina
authors.affiliation: Department of Pharmaceutical Chemistry, University of Vienna, Althanstraße 14, Vienna A-1090, Austria. norbert.haider@univie.ac.at.
authors.forename: Norbert
authors.initials: N
authors.lastname: Haider
authors.affiliation: Institute of Structural Biology, Helmholtz Zentrum München-Research Center for Environmental Health (GmbH), Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany. itetko@vcclab.org.|BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, Neuherberg D-85764, Germany. itetko@vcclab.org.
authors.forename: Igor V
authors.initials: IV
authors.lastname: Tetko
mesh_terms: D062126:Databases, Chemical; D012984:Software; D013329:Structure-Activity Relationship
keywords: chemical functional groups; chemoinformatics analysis; data interpretation; heterocyclic compounds; machine learning