Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines.
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.
Liao
Quan
Q
Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 354, Fenglin Road, Shanghai 200032, China.
Yao
Jianhua
J
Yuan
Shengang
S
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