Classification structure-activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives.
The grid search support vector machine (GS-SVM) was used to build a classification structure-activity relationship (CSAR) model and to predict the genotoxicity property of 140 thiophene derivatives with the information derived from the compounds' molecular structures. The seven descriptors selected by linear discriminant analysis (LDA) were used as the inputs to develop the GS-SVM model. Using the Grid Search method, a satisfactory model with a good predictive capability was obtained. The quality of the models was evaluated by the number of right classified compounds. The total accuracy of the LDA model was 81.4% and 85.2% for the training set and test set, respectively, and to the GS-SVM model was 92.9% and 92.6%, respectively. It was proved that the GS-SVM method was a very useful modeling approach with good classification ability for the genotoxicity of the thiophene derivatives. This work also provides a new idea and an alternative method to investigate the genotoxicity of the similar structures with thiophene derivatives, and can be extended to other toxicity studies.
Du
Hongying
H
Department of Chemistry, Lanzhou University, Lanzhou, China.
Wang
Jie
J
Watzl
June
J
Zhang
Xiaoyun
X
Hu
Zhide
Z
D000818:Animals; D016002:Discriminant Analysis; D009153:Mutagens / Q000737:chemistry / Q000145:classification / Q000633:toxicity*; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D051381:Rats; D015203:Reproducibility of Results; D000091243:Ribosomal Protein S9; D012269:Ribosomal Proteins / Q000187:drug effects / Q000378:metabolism; D013014:SOS Response, Genetics / Q000187:drug effects / Q000235:genetics; D013329:Structure-Activity Relationship; D013876:Thiophenes / Q000737:chemistry / Q000145:classification / Q000633:toxicity*
D009153:Mutagens; D000091243:Ribosomal Protein S9; D012269:Ribosomal Proteins; D013876:Thiophenes