Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds

Chow, Hsiao-Hui and Chen, Hsinchun and Ng, Tobun Dorbin and Myrdal, P. and Yalkowsky, S.H. (1995) Using Backpropagation Networks for the Estimation of Aqueous Activity Coefficients of Aromatic Organic Compounds. Journal of Chemical Information and Computer Sciences, American Chemical Society 35(4):pp. 723-728.

Abstract

This research examined the applicability of using a neural network approach to the estimation of aqueous activity coefficients of aromatic organic compounds from fragmented structural information. A set of 95 compounds was used to train the neural network, and the trained network was tested on a set of 31 compounds. A comparison was made between the results and those obtained using multiple linear regression analysis. With the proper selection of neural network parameters, the backpropagation network provided a more accurate prediction of the aqueous activity coefficients for testing data than did regression analysis. This research indicates that neural networks have the potential to become a useful analytical technique for quantitative prediction of structure-activity relationships.