How far are molecular connectivity descriptors from I(S) molecular pseudoconnectivity descriptors?

Area of Science:

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

Background:

• Molecular connectivity indices (MCI) are widely used in QSAR for predicting chemical properties.
• Intrinsic-state pseudoconnectivity indices (IPCI) offer an alternative descriptor set.
• Comparing the predictive power of MCI and IPCI is crucial for advancing QSAR modeling.

Purpose of the Study:

• To compare the modeling capabilities of MCI and IPCI across various compound classes and properties.
• To investigate the impact of molar mass as an additional descriptor.
• To explore the influence of rescaling and descriptor degeneracy on modeling quality.

Main Methods:

• Application of MCI and IPCI to model activities of chlorofluorocarbons, phenethylamines, and benzimidazoles.
• Modeling of boiling points for primary amines and alcohols, including simultaneous modeling.
• Analysis of molar mass and descriptor degeneracy effects on model performance.

Main Results:

• IPCI demonstrated advantageous descriptive power compared to MCI for many compound classes.
• Molar mass inclusion showed varying effects depending on the specific property and compound class.
• Mixed higher-order connectivity-pseudoconnectivity terms were identified, consistently improving model quality.

Conclusions:

• IPCI represent a valuable alternative or complement to MCI in QSAR modeling.
• The development of novel mixed connectivity-pseudoconnectivity terms enhances predictive accuracy.
• These findings contribute to more robust and accurate computational modeling in chemistry.