Predicting hexadecane-air equilibrium partition coefficients (L) using a group contribution approach constructed from...

Area of Science:

• Environmental Chemistry
• Computational Chemistry
• Physical Chemistry

Background:

• Quantitative Structure-Property Relationships (QSPR) are crucial for predicting chemical properties.
• Accurate prediction of hexadecane-air partition coefficients (L) is important for environmental fate modeling.
• Existing QSPR models often struggle with structurally complex organic chemicals.

Purpose of the Study:

• To develop a robust group contribution-based QSPR model for hexadecane-air equilibrium partition coefficients (L).
• To enhance the predictive accuracy for a diverse range of organic chemicals, including complex structures.
• To improve predictions for chemicals exhibiting non-additive partitioning effects due to intramolecular hydrogen bonds.

Main Methods:

• Utilized a group contribution approach for QSPR model development.
• Employed the iterative fragment selection (IFS) approach for model training.
• Incorporated a large dataset of L values for structurally complex chemicals measured consistently.
• Validated the model using an external dataset.

Main Results:

• The developed QSPR model demonstrates superior predictive power compared to existing methods.
• The model performs significantly better for structurally complex chemicals.
• Exceptional performance was observed for chemicals with intramolecular hydrogen bonds, addressing non-additive effects.
• The QSPR model shows improved accuracy over methods trained on simpler chemical structures or diverse data sources.

Conclusions:

• The new group contribution-based QSPR model offers enhanced accuracy for predicting hexadecane-air partition coefficients.
• This approach is particularly effective for complex organic molecules and those with intramolecular hydrogen bonding.
• The developed model provides a valuable tool for environmental risk assessment and chemical property prediction.