Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds

• 939 KOA data for 379 compounds at 263.15–323.15 K are collected from literature.
• Three methods are systematically developed and evaluated for KOA prediction.
• An optimal QSAR model is recommended for predicting KOA at various temperatures.
• It is the first time to use the ab initio SM8AD solvation model for KOA prediction.

The octanol-air partition coefficient (KOA) is needed for assessing multimedia transport and bioaccumulability of organic chemicals in the environment. As experimental determination of KOA for various chemicals is costly and laborious, development of KOA estimation methods is necessary. We investigated three methods for KOA prediction, conventional quantitative structure–activity relationship (QSAR) models based on molecular structural descriptors, group contribution models based on atom-centered fragments, and a novel model that predicts KOA via solvation free energy from air to octanol phase (ΔGO0), with a collection of 939 experimental KOA values for 379 compounds at different temperatures (263.15–323.15 K) as validation or training sets. The developed models were evaluated with the OECD guidelines on QSAR models validation and applicability domain (AD) description. Results showed that although the ΔGO0 model is theoretically sound and has a broad AD, the prediction accuracy of the model is the poorest. The QSAR models perform better than the group contribution models, and have similar predictability and accuracy with the conventional method that estimates KOA from the octanol–water partition coefficient and Henry's law constant. One QSAR model, which can predict KOA at different temperatures, was recommended for application as to assess the long-range transport potential of chemicals.

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