On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• Quantitative Structure-Property Relationship (QSPR) modeling is crucial for predicting drug properties.
• Topological descriptors offer a mathematical representation of molecular structures.
• Accurate QSPR models are essential for efficient drug design and development.

Purpose of the Study:

• To investigate the utility of degree-based topological indices in QSPR modeling for pulmonary cancer drugs.
• To assess the predictive accuracy of these descriptors for key physicochemical properties.
• To highlight the potential of computational chemistry in accelerating drug discovery.

Main Methods:

• Degree-based topological indices were calculated using Python-based computational methods.
• Linear regression models were employed for analysis using SPSS software.
• Physicochemical properties including boiling point, flash point, molar refractivity, and polarizability were predicted.

Main Results:

• Excellent correlations were observed between computed topological indices and physicochemical properties.
• The predictive accuracy was high for most properties, with notable exceptions for flash point.
• Specific topological indices demonstrated superior predictive performance for certain properties.

Conclusions:

• Degree-based topological indices are reliable predictors in QSPR modeling for pulmonary cancer drugs.
• Computational and mathematical chemistry integration streamlines preclinical drug evaluation.
• This approach provides a foundation for designing more effective cancer treatments and reducing drug discovery costs.