Non a Priori Automatic Discovery of 3D Chemical Patterns: Application to Mutagenicity. Issue 10 (7th June 2017)

• Record Type: Journal Article
• Authors: Rabatel, Julien; Fannes, Thomas; Lepailleur, Alban; Le Goff, Jérémie; Crémilleux, Bruno; Ramon, Jan; Bureau, Ronan; Cuissart, Bertrand
• Is Part Of: Molecular informatics. Volume 36:Issue 10(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-06-07
• Subjects: Geometrical pattern -- Random Forest -- Structure-activity relationships -- Structural Alerts -- Toxicology; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201700022
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: 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. Abstract :