title: Benchmark data set for in silico prediction of Ames mutagenicity.
abstract: Up to now, publicly available data sets to build and evaluate Ames mutagenicity prediction tools have been very limited in terms of size and chemical space covered. In this report we describe a new unique public Ames mutagenicity data set comprising about 6500 nonconfidential compounds (available as SMILES strings and SDF) together with their biological activity. Three commercial tools (DEREK, MultiCASE, and an off-the-shelf Bayesian machine learner in Pipeline Pilot) are compared with four noncommercial machine learning implementations (Support Vector Machines, Random Forests, k-Nearest Neighbors, and Gaussian Processes) on the new benchmark data set.
authors.affiliation: University of Technology, Berlin, Germany.
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mika
authors.forename: Timon
authors.initials: T
authors.lastname: Schroeter
authors.forename: Andreas
authors.initials: A
authors.lastname: Sutter
authors.forename: Antonius
authors.initials: A
authors.lastname: ter Laak
authors.forename: Thomas
authors.initials: T
authors.lastname: Steger-Hartmann
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Heinrich
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D001185:Artificial Intelligence; D019985:Benchmarking; D019295:Computational Biology; D016208:Databases, Factual; D009152:Mutagenicity Tests / Q000379:methods* / Q000592:standards; D009153:Mutagens / Q000737:chemistry / Q000633:toxicity; D016011:Normal Distribution; D012486:Salmonella typhimurium / Q000187:drug effects / Q000235:genetics; D013329:Structure-Activity Relationship
chemical_list: D009153:Mutagens