In order to investigate the relationship between activities and structures, a 3D-QSAR study is applied to a set of 43 halogenated phenols compounds. This study was conducted using the principal component analysis (PCA) method, the multiple linear regression method (MLR), the non-linear regression (RNLM) and the artificial neural network (ANN). We accordingly propose a quantitative model, and we interpret the activity of the compounds relying on the multivariate statistical analysis. Density functional theory (DFT) and ab-initio molecular orbital calculations have been carried out in order to get insights into the structure, chemical reactivity and property information for the series of study compounds. This study shows that the MRA and MNLR have served also to predict activities, but when compared with the results given by the ANN, we realized that the predictions fulfilled by this latter were more effective. The obtained results suggested that the proposed combination of several calculated parameters could be useful to predict the biological activity of halogenated phenols over Tetrahymenapyriformis.
