Title :
QSAR Studies on Toxicity of Organic Compounds to Chlorella vulgaris
Author :
Lv, Xin-Qi ; Zhang, Yun-Tao
Author_Institution :
Inst. of Appl. Chem., China West Normal Univ., Nanchong, China
Abstract :
The quantitative structure-activity relationships (QSAR) studies on toxicity of 91 organic compounds to Chlorella vulgaris have been performed by using ν-support vector machine(ν-SVM) algorithm and taking the 2D-autocorrelation descriptors as the structural parameters based on variable selection with particle swarm optimization(PSO) methed. The correlation coefficient(R2) and Qcv2 of PSO-ν-SVM model in the training set are respectively 0.9469 and 0.7216, and Ra2 in the test set is 0.9446 while the R2, Qcv2 and Ra2 of training set and test set of the reference model are 0.9340, 0.9090 and 0.9290, respectively. The result shows that the QSAR model has better stability and prediction ability, so this model is a good reference for the study on toxicity of organic compounds to Chlorella vulgaris.
Keywords :
biology computing; microorganisms; organic compounds; particle swarm optimisation; support vector machines; toxicology; 2D-autocorrelation descriptors; Chlorella vulgaris; QSAR; correlation coefficient; organic compound toxicity; particle swarm optimization; prediction ability; quantitative structure-activity relationship; structural parameters; training set; v-support vector machine algorithm; variable selection; Biological system modeling; Correlation; Data models; Input variables; Organic compounds; Testing; Training; Chlorella vulgaris; PSO; QSAR; v-SVM;
Conference_Titel :
Intelligent Systems (GCIS), 2010 Second WRI Global Congress on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9247-3
DOI :
10.1109/GCIS.2010.29