A new QSAR approach based on a Quantitative Neighbourhoods of Atoms description of molecular structures and self-consistent regression was developed. Its prediction accuracy, advantages and limitations were analysed from three sets of published experimental data on acute toxicity: 56 phenylsulfonyl carboxylates for Vibrio fischeri; 65 aromatic compounds for the alga Chlorella vulgaris and 200 phenols for the ciliated protozoan Tetrahymena pyriformis. According to our findings, the proposed approach provides a good correlation and prediction accuracy (r2=0.908 and Q2=0.866) for the set of 56 phenylsulfonyl carboxylates and the 65 aromatic compounds tested on C. vulgaris (r2=0.885, Q2=0.849). For the 200 phenols tested on T. pyriformis, the prediction accuracy was r2¼0.685 and Q2¼0.651. This is at least as good as the best results obtained with the other QSAR methods originally used on the same data sets.
SAR & QSAR in Environmental Research, 2007, 18 (3-4), 285-298.