Quantitative structure–activity relationships study of herbicides using neural networks and different statistical methods

A series of herbicidal materials, N-phenylacetamides (NPAs), has been studied for their Quantitative Structure–Activity Relationships (QSAR). The molecular structure as well as the activity data were taken from literature [O. Kirino, C. Takayama, A. Mine, Quantitative structure relationships of herbicidal N-(1-methyl-1-phenylethyi) phenylacetamides, Journal Pesticide Science 11 (1986) 611–617]. The independent variables used to describe the structure of compounds consisted of seven physicochemical properties, including the mode of molecular connection, steric factor, hydrophobic parameter, etc. Fifty different compounds constitute a sample set which is divided into two groups, 47 of them form a training set and the remaining three a checking set. Through a systematic study by using the classic multivariate analysis such as the Multiple Linear Regression (MLR), the Principal Component Analysis (PCA), and the Partial Least Squares (PLS) Regression, several QSAR models were established. For finding a better way to depict the nonlinear nature of the problem, multi-layered feed-forward (MLF) neural networks (NNs) was employed. The results indicated that the conventional multivariate analysis gave larger prediction errors, while the NNs method showed better accuracy in both self-checking and prediction-checking. The error variance of predictions made by NNs was the smallest among the all methods tested, only around half of the others.