A method for automated molecular optimization applied to Ames mutagenicity data.
The present work describes a method that optimizes a compound on the basis of the interpretation of quantitative structure-activity relationship models. The method has been applied to query compounds that have a mutagenicity liability. The substructure that contributes the most to the mutagenicity prediction is identified and replaced for each query compound. Replacement substructures have been generated in a deterministic fashion to produce a range of new, nonmutagen, compounds. A portion of the new compounds already exists in literature, but was unknown to the method during optimization. These results suggest that this method can substitute "toxic" substructures and produce libraries of compounds with lower liability for a given endpoint. This method is intended to complement or replace the database searches that chemists need to undertake when trying to avoid safety problems in compounds.
Helgee
Ernst Ahlberg
EA
Safety Assessment, AstraZeneca Research & Development, 43183 Mölndal, Sweden. ernst.ahlberghelgee@astrazeneca.com
Carlsson
Lars
L
Boyer
Scott
S
D001331:Automation; D009152:Mutagenicity Tests; D021281:Quantitative Structure-Activity Relationship