《ci900161g_mutagen_二零一六》.pdf

J. Chem. Inf. Model. 2009, 49, 2077–2081 2077 Benchmark Data Set for in Silico Prediction of Ames Mutagenicity Katja Hansen,† Sebastian Mika,‡ Timon Schroeter,† Andreas Sutter,§ Antonius ter Laak,§ Thomas Steger-Hartmann,§ Nikolaus Heinrich,§ and Klaus-Robert Mu¨ller*,† University of Technology, Berlin, Germany, idalab GmbH, Berlin, Germany, and Bayer Schering Pharma AG, Berlin, Germany Received May 6, 2009 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. 1. INTRODUCTION sources. As described in this report, we make this large unique 1-4 benchmark set - including well-defined random splits - publicly The bacterial reverse mutation assay (Ames test ) to available (see http://ml.cs.tu-berlin.de/toxbenchmark/) to facili- detect mutagenicity in vitro is of crucial importance in drug