Conformation-dependent QSPR models: logPOW.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Quantitative Structure-Property Relationships (QSPR)

Background:

• Predicting the octanol-water partition coefficient (logP(OW)) is crucial for understanding drug absorption, distribution, metabolism, and excretion (ADME).
• Existing QSPR models often rely on global molecular descriptors, potentially overlooking localized chemical properties.

Purpose of the Study:

• To develop novel QSPR models for predicting logP(OW) using local molecular surface properties.
• To assess the impact of molecular conformation on predictive accuracy.
• To identify the primary sources of error in logP(OW) prediction.

Main Methods:

• Calculation of local molecular properties at the standard isodensity surface using semiempirical molecular orbital theory.
• Development of QSPR models utilizing surface area descriptors derived from a predefined binning scheme.
• Inclusion of conformational effects in model development and evaluation.

Main Results:

• The developed QSPR models effectively predict logP(OW) based on local surface properties.
• Molecular conformation was found to have a minimal impact on the predictive performance of the models.
• Error analysis indicated that experimental data accuracy is the main limitation, with optimal performance around ±0.5 log units.

Conclusions:

• QSPR models based on local surface properties provide a valuable approach for predicting logP(OW).
• The models generate a local hydrophobicity function, offering insights into molecular surface characteristics.
• The inherent accuracy of experimental logP(OW) data sets a benchmark for predictive model performance.