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Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.
Zarei, Kobra; Atabati, Morteza; Kor, Kamalodin.
Afiliación
  • Zarei K; School of Chemistry, Damghan University, Damghan, Iran, zarei@du.ac.ir.
Bull Environ Contam Toxicol ; 92(6): 642-9, 2014 Jun.
Article en En | MEDLINE | ID: mdl-24638918
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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Benceno / Algoritmos / Pruebas de Toxicidad / Relación Estructura-Actividad Cuantitativa Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Bull Environ Contam Toxicol Año: 2014 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Contaminantes Químicos del Agua / Benceno / Algoritmos / Pruebas de Toxicidad / Relación Estructura-Actividad Cuantitativa Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Bull Environ Contam Toxicol Año: 2014 Tipo del documento: Article