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.

journal_name

J Chem Inf Model

authors

Hansen K,Mika S,Schroeter T,Sutter A,ter Laak A,Steger-Hartmann T,Heinrich N,Müller KR

doi

10.1021/ci900161g

subject

Has Abstract

pub_date

2009-09-01 00:00:00

pages

2077-81

issue

9

eissn

1549-9596

issn

1549-960X

journal_volume

49

pub_type

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