ci300400a_si_001.xls (729 kB)
In silico Prediction of Chemical Ames Mutagenicity
dataset
posted on 2012-11-26, 00:00 authored by Congying Xu, Feixiong Cheng, Lei Chen, Zheng Du, Weihua Li, Guixia Liu, Philip
W. Lee, Yun TangMutagenicity is one of the most important end points
of toxicity.
Due to high cost and laboriousness in experimental tests, it is necessary
to develop robust in silico methods to predict chemical
mutagenicity. In this paper, a comprehensive database containing 7617
diverse compounds, including 4252 mutagens and 3365 nonmutagens, was
constructed. On the basis of this data set, high predictive models
were then built using five machine learning methods, namely support
vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural
network (ANN), k-nearest neighbors (kNN), and naïve Bayes (NB), along with five fingerprints, namely
CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS),
PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP).
Performances were measured by cross validation and an external test
set containing 831 diverse chemicals. Information gain and substructure
analysis were used to interpret the models. The accuracies of fivefold
cross validation were from 0.808 to 0.841 for top five models. The
range of accuracy for the external validation set was from 0.904 to
0.980, which outperformed that of Toxtree. Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed
high and reliable predictive accuracy for the mutagens and nonmutagens
and, hence, could be used in prediction of chemical Ames mutagenicity.
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FPSVMsupport vector machinechemical Ames mutagenicity3365 nonmutagenssilico PredictionChemical Ames MutagenicityMutagenicityCDK fingerprintsilico methodsANNSubstructure fingerprintinformation gainchemical mutagenicityMACCSNB4252 mutagensPubChemend pointsDT4.5accuracysubstructure analysisEstatemodelvalidation
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