title: Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.
abstract: A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis.
authors.affiliation: School of Chemistry, Damghan University, Damghan, Iran, zarei@du.ac.ir.
authors.forename: Kobra
authors.initials: K
authors.lastname: Zarei
authors.forename: Morteza
authors.initials: M
authors.lastname: Atabati
authors.forename: Kamalodin
authors.initials: K
authors.lastname: Kor
mesh_terms: D000465:Algorithms; D000818:Animals; D001554:Benzene / Q000633:toxicity*; D017143:Fuzzy Logic; D008956:Models, Chemical; D021281:Quantitative Structure-Activity Relationship; D013769:Tetrahymena pyriformis; D018675:Toxicity Tests / Q000379:methods*; D014874:Water Pollutants, Chemical / Q000633:toxicity*
chemical_list: D014874:Water Pollutants, Chemical; D001554:Benzene