Molecular connectivity. III: Relationship to partition coefficients.

Area of Science:

• Quantitative Structure-Activity Relationships (QSAR)
• Computational Chemistry
• Medicinal Chemistry

Background:

• Octanol-water partition coefficients (LogP) are crucial for predicting drug absorption, distribution, metabolism, and excretion (ADME).
• Molecular descriptors are needed to model and predict physicochemical properties like LogP.
• The molecular connectivity index (MCI) is a topological descriptor that captures molecular structure.

Purpose of the Study:

• To investigate the linear relationship between the molecular connectivity index and octanol-water partition coefficients.
• To explore a modified connectivity index for improved correlation across diverse chemical classes.
• To assess the utility of the connectivity index in correlating biological activity.

Main Methods:

• Calculation of the molecular connectivity index for various monofunctional chemical classes.
• Linear regression analysis to establish correlations between MCI and LogP.
• Development and application of a modified connectivity index considering atomic valency and unsaturation.

Main Results:

• A strong linear relationship was observed between MCI and LogP for esters, alcohols, ketones, ethers, carboxylic acids, and amines.
• A modified MCI, incorporating valency and unsaturation, successfully merged data for most compounds, excluding hydrocarbons.
• The connectivity index demonstrated utility in correlating biological activity data.

Conclusions:

• The molecular connectivity index is a valuable descriptor for predicting octanol-water partition coefficients.
• The modified connectivity index offers a more universal approach for correlating physicochemical properties.
• Partition coefficients serve as an intermediate link between molecular structure and biological activity.