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Abstract
Knowledge of the interpolative region or applicability domain (AD) of structure–activity relationships is believed to improve predictive accuracy. The present work was undertaken to characterize the AD of EPI Suite™ biotransformation models and evaluate the performance of selected AD assessment methods. AD methods were applied to the training sets of four models representing different end-points, and the predictive accuracy was then evaluated using six independent validation sets. Two of the models estimated a continuous variable (log half-life) from fragment descriptors. For biotransformation in fish (BCFBAF™) and hydrocarbon biodegradation (BioHCwin), the approach using ranges, with preprocessing by analysis of principal components, worked reasonably well in identifying subsets of validation chemicals that have higher root mean squared error than for all validation chemicals. AD methods were also applied to two classification models, Biowin3 (which predicts the time required to achieve complete aerobic biodegradation) and Biowin5 (the probability of ready biodegradation in the OECD 301C test). Structure-based AD methods (fingerprints, atom environments) showed some success, but descriptor-based AD methods were not useful in identifying misclassified chemicals. For Biowin3 the largest percentage of misclassified chemicals was obtained for chemicals for which prediction was based on molecular weight alone, which suggests the need to revise the fragment library of the model.