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QSAR Models of Reaction Rate Constants of Alkenes with Ozone and Hydroxyl Radical
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As constantes de velocidade da rea??o do oz?nio com 95 alcenos (-logk_(O3)) e do radical hidroxila (?OH) com 98 alcenos (-logk_(OH)) na atmosfera foram previstas por modelos de rela??es quantitativas entre estrutura e atividade (QSAR). Cálculos usando a teoria do funcional da densidade (DFT) foram realizados para os respectivos alcenos no estado fundamental e para as estruturas do estado de transi??o para o processo de degrada??o na atmosfera. Técnicas de regress?o linear múltipla (MLR) e de redes neurais de regress?o generalizada (GRNN) foram utilizadas para desenvolver os modelos. O modelo GRNN de -logkO_3 com base em três descritores e propaga??o ideal σ de 0,09 tem erro quadrático médio (rms) de 0,344; o modelo GRNN de -logkOH com quatro descritores e propaga??o ideal σ de 0,14 produz um erro rms de 0,097. Comparado com os modelos da literatura, os modelos GRNN neste artigo mostram estatísticas melhores. A importancia dos descritores associados aos estados de transi??o na previs?o de k_(O3) e k_(OH) nos processos de degrada??o atmosférica foi demonstrada. The reaction rate constants of ozone with 95 alkenes (-logk_(O3)) and the hydroxyl radical (?OH) with 98 alkenes (-logk_(OH)) in the atmosphere were predicted by quantitative structure-activity relationship (QSAR) models. Density functional theory (DFT) calculations were carried out on respective ground-state alkenes and transition-state structures of degradation processes in the atmosphere. Stepwise multiple linear regression (MLR) and general regression neural network (GRNN) techniques were used to develop the models. The GRNN model of -logk_(O3) based on three descriptors and the optimal spread σ of 0.09 has the mean root mean square (rms) error of 0.344; the GRNN model of -logk_(OH) having four descriptors and the optimal spread σ of 0.14 produces the mean rms error of 0.097. Compared with literature models, the GRNN models in this article show better statistical characteristics. The importance of transition state descriptors in predicting k_(O3) and k_(OH) of atmospheric degradation processes has been demonstrated.
atmospheric degradationgeneral regression neural networkquantitative structure-activity relationshipreaction rate constanttransition states
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