Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.
The soil sorption partition coefficient logKocis an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logKocfor new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logKocof known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.
Faculty of Civil Engineering, K.N.Toosi University of Technology, No. 1346, Vali-e-asr Street, 19967-15433, Tehran, Iran. Electronic address: sabour@kntu.ac.ir.
Mohammad Reza
MR
Sabour
Faculty of Civil Engineering, K.N.Toosi University of Technology, No. 1346, Vali-e-asr Street, 19967-15433, Tehran, Iran.
Saman
S
Moftakhari Anasori Movahed
D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry*; D012987:Soil / Q000737:chemistry*; D012989:Soil Pollutants / Q000032:analysis*
D009930:Organic Chemicals; D012987:Soil; D012989:Soil Pollutants
Artificial Neural Network; Generalized Regression Neural Network (GRNN); Soil sorption; Topological descriptors