Application of RBF nerual network into the Kow of chemical contaminants

The octanol/water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol/water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the RBF network to predict, the correlation coefficient has achieved 1.000, the prediction error in the permission scope.