Benchmark data set for in silico prediction of Ames mutagenicity.
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.
Hansen
Katja
K
University of Technology, Berlin, Germany.
Mika
Sebastian
S
Schroeter
Timon
T
Sutter
Andreas
A
ter Laak
Antonius
A
Steger-Hartmann
Thomas
T
Heinrich
Nikolaus
N
Müller
Klaus-Robert
KR
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
D009153:Mutagens