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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Hansen K,Mika S,Schroeter T,Sutter A,ter Laak A,Steger-Hartmann T,Heinrich N,Müller KRdoi
10.1021/ci900161gsubject
Has Abstractpub_date
2009-09-01 00:00:00pages
2077-81issue
9eissn
1549-9596issn
1549-960Xjournal_volume
49pub_type
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