title: Evolving interpretable structure-activity relationships. 1. Reduced graph queries.
abstract: A new machine learning method is presented for extracting interpretable structure-activity relationships from screening data. The method is based on an evolutionary algorithm and reduced graphs and aims to evolve a reduced graph query (subgraph) that is present within the active compounds and absent from the inactives. The reduced graph representation enables heterogeneous compounds, such as those found in high-throughput screening data, to be captured in a single representation with the resulting query encoding structure-activity information in a form that is readily interpretable by a chemist. The application of the method is illustrated using data sets extracted from the well-known MDDR data set and GSK in-house screening data. Queries are evolved that are consistent with the known SARs, and they are also shown to be robust when applied to independent sets that were not used in training.
authors.affiliation: Department of Information Studies, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield, United Kingdom.
authors.forename: Kristian
authors.initials: K
authors.lastname: Birchall
authors.forename: Valerie J
authors.initials: VJ
authors.lastname: Gillet
authors.forename: Gavin
authors.initials: G
authors.lastname: Harper
authors.forename: Stephen D
authors.initials: SD
authors.lastname: Pickett
mesh_terms: D000465:Algorithms; D002875:Chromosomes / Q000235:genetics; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D006801:Humans; D010641:Phenotype; D044282:Receptor, Serotonin, 5-HT1A / Q000378:metabolism; D058825:Serotonin 5-HT1 Receptor Agonists; D013329:Structure-Activity Relationship
chemical_list: D058825:Serotonin 5-HT1 Receptor Agonists; D044282:Receptor, Serotonin, 5-HT1A