Document Type : Original Research Article

Author

• Samirah H. Alsulami

Department of Mathematics and Statistics, College of Science, University of Jeddah, Jeddah, Saudi Arabia

Abstract

Medical researchers face a significant hurdle such as time commitment, large amount of costs, establishing a safety profile for medications, reduced solubility, and insufficient experimental data while producing innovative drugs. By studying the structural properties of molecules, chemical graph theory plays a significant role in advancing drug development and design. To improve drug research and assess the effectiveness of treatments, degree-based topological indices play a crucial role in Quantitative Structure-Property Relationship (QSPR) analysis, aiding in the estimation of their properties and potential efficacy. In this article, ten reducible degree-based topological indices are calculated and QSPR analysis for 15 stroke drugs (Atenolol, Baclofen, Dapsone, Diclofenac, Dopamine, etc.) to correlate with seven physicochemical properties (Molar mass, Melting point, XlogP, Complexity, LogP, Vapor pressure, and Solubility) using linear regression model. This methodology enhances drug discovery and development for strokes by offering valuable insights into the correlation between molecular structure and pharmacological properties. This theoretical technique might help chemists and pharmaceutical industry workers to predicted stroke drug properties without expensive testing.

Graphical Abstract

Keywords

• Stroke drugs
• topological indices
• regression models
• QSPR analysis

Main Subjects

• Physical chemistry