Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity

Julien Rabatel 1 Fannes Thomas Alban Lepailleur 2 Jérémie Le Goff 3 Bruno Crémilleux 4 Jan Ramon Ronan Bureau 2 Bertrand Cuissart 4
1 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
4 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This article introduces a new type of structural fragment called a geometrical pattern. Such geometrica lpatterns are defined as molecular graphs that include a labelling of atoms together with constraints on interatomic distances. The discovery of geometrical patterns in a chemical dataset relies on the induction of multiple decision trees combined in random forests. Each computational step corresponds to a refinement of a preceding set of constraints, extending a previous geometrical pattern. This paper focuses on the mutagenicity of chemicals via the definition of structural alerts in relation with these geometrical patterns. It follows an experimental assessment of the main geometrical patterns to show how they can efficiently originate the definition of a chemical feature related to a chemical function or a chemical property. Geometrical patterns have provided a valuable and innovative approach to bring new pieces of information for discovering and assessing structural characteristics in relation to a particular biological phenotype.
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Article dans une revue
Molecular Informatics, Wiley-VCH, 2017, 〈10.1002/minf.201700022〉
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https://hal.archives-ouvertes.fr/hal-01576894
Contributeur : Bertrand Cuissart <>
Soumis le : jeudi 24 août 2017 - 11:52:40
Dernière modification le : vendredi 8 juin 2018 - 08:51:57

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Julien Rabatel, Fannes Thomas, Alban Lepailleur, Jérémie Le Goff, Bruno Crémilleux, et al.. Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity. Molecular Informatics, Wiley-VCH, 2017, 〈10.1002/minf.201700022〉. 〈hal-01576894〉

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