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. A series of herbicidal materials, N-phenylacetamides NPAs , has been studied for their Quantitative Structure-Activity Ž . w Relationships QSAR . The molecular structure as well as the activity data were taken from literature O. Kirino, C. Takayama, Ž . A. Mine, Quantitative structure relationships of herbicidal N-1-methyl-1-phenylethyi phenylacetamides, Journal Pesticide Ž .
x 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 Re-Ž . Ž . Ž . gression 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 Ž . Ž . 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.