Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

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• 作者：Katja Hansen ; Sebastian Mika ; Timon Schroeter ; Andreas Sutter ; Antonius ter Laak ; Thomas Steger-Hartmann ; Nikolaus Heinrich ; Klaus-Robert Mller
• 刊名：Journal of Chemical Information and Modeling
• 出版年：2009
• 出版时间：September 28, 2009
• 年：2009
• 卷：49
• 期：9
• 页码：2077-2081
• 全文大小：121K
• 年卷期：v.49,no.9(September 28, 2009)
• ISSN：1549-960X

文摘

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.