Predicting octanol/water partition coefficient using solvation free energy and solvent-accessible surface area

Area of Science:

• Computational Chemistry
• Environmental Science
• Physical Chemistry

Background:

• Octanol/water partition coefficient (Kow) is crucial for understanding chemical behavior in biological and environmental systems.
• Accurate prediction of Kow is essential for risk assessment and environmental fate studies.
• Existing methods may require extensive experimental data or complex calculations.

Purpose of the Study:

• To develop a simplified regression model for predicting octanol/water partition coefficients (Kow).
• To utilize readily available quantum chemical descriptors: solvation free energy (delta Gs) and solvent-accessible surface area (SASA).
• To establish a computationally efficient method for estimating partition properties.

Main Methods:

• Development of a regression model incorporating solvation free energy (delta Gs) and solvent-accessible surface area (SASA).
• Validation of the model using a dataset of 47 organic compounds across 17 diverse types.
• Quantum chemical calculations were employed to derive the molecular descriptors.

Main Results:

• The developed regression model achieved a high correction coefficient (adjusted for degrees of freedom) of 0.959.
• The model demonstrated a low standard error of 0.277 log units for Kow prediction.
• The model effectively captures partition properties related to solute-solvent interactions in the water phase.

Conclusions:

• The proposed model provides a suitable and accurate method for predicting octanol/water partition coefficients (Kow).
• The reliance on only two quantum chemical descriptors (delta Gs and SASA) simplifies the prediction process.
• This approach is valuable for estimating chemical partitioning and understanding interactions in aqueous systems.