A graph-based computational approach for modeling physicochemical properties in drug design

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Physicochemical properties dictate drug stability, bioavailability, and therapeutic efficacy.
• Understanding structure-property relationships is crucial for drug design and development.

Purpose of the Study:

• To predict key physicochemical properties of antibiotics and neuropathic drugs using mathematical modeling.
• To explore the utility of quantitative structure-property relationship (QSPR) analysis for drug optimization.

Main Methods:

• Utilized modified degree-based topological indices as molecular descriptors.
• Employed linear and quadratic regression models for quantitative structure-property relationship (QSPR) analysis.
• Predicted physicochemical properties including boiling point, enthalpy of vaporization, flash point, and molar refraction.

Main Results:

• Quadratic regression models demonstrated superior predictive performance compared to linear models for most properties.
• High R-squared values and low error margins indicated excellent model accuracy.
• Topological descriptors effectively correlated molecular structure with physicochemical properties.

Conclusions:

• Mathematical modeling and QSPR analysis, particularly with quadratic models, are powerful tools for predicting drug physicochemical properties.
• Topological descriptors offer a valuable approach for early-stage drug screening and optimization.
• This methodology can accelerate the development of effective antibiotics and neuropathic drugs.