title: Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines.
abstract: 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.
authors: Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 354, Fenglin Road, Shanghai 200032, China.
authors: Quan
authors: Q
authors: Liao
authors: Jianhua
authors: J
authors: Yao
authors: Shengang
authors: S
authors: Yuan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003663:Decision Trees; D008954:Models, Biological; D008962:Models, Theoretical; D009152:Mutagenicity Tests / Q000379:methods*; D021281:Quantitative Structure-Activity Relationship