基因表达式编程用于有机化合物的毒性预测

Gene Expression Programming for Toxicity Prediction of Organic Compounds

基因表达式编程方法(GEP)是一种新型的数据挖掘和建模工具,应用GEP方法对110个有机化合物的毒性进行了构效关系研究,并与人工神经网络(BP-ANN)和偏最小二乘(PLS)方法比较.结果发现,GEP方法的预测较好,且模型稳定.

Gene expression programming（ GEP）,a relatively new evolutionary algorithm,can be used to data mining and modeling. In this paper,the GEP was applied to quantitative structure-activity relationship（ QSAR） analysis of toxicity prediction of organic compounds. The results were compared with those obtained by the artificial neural network and partial least squares. The comparison demonstrated that GEP is a useful tool for QSAR analysis and the models are steady.