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Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity
Author(s) -
Rabatel Julien,
Fannes Thomas,
Lepailleur Alban,
Le Goff Jérémie,
Crémilleux Bruno,
Ramon Jan,
Bureau Ronan,
Cuissart Bertrand
Publication year - 2017
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201700022
Subject(s) - cheminformatics , a priori and a posteriori , computer science , relation (database) , set (abstract data type) , feature (linguistics) , property (philosophy) , function (biology) , fragment (logic) , data mining , biological system , theoretical computer science , algorithm , chemistry , computational chemistry , biology , philosophy , linguistics , epistemology , evolutionary biology , programming language
This article introduces a new type of structural fragment called a geometrical pattern . Such geometrical patterns 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.