Journal
MOLECULAR DIVERSITY
Volume 11, Issue 2, Pages 59-72Publisher
SPRINGER
DOI: 10.1007/s11030-007-9057-5
Keywords
prediction; mutagenic toxicity; substructural descriptor; recursive partitioning; support vector machines
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The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure-toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL's toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.
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