Quantitative structure property relationship and multiattribute decision analysis of antianginal drugs using topological indices

Abstract

Angina is a condition characterized by chest pain or discomfort due to insufficient blood flow to the heart muscle. Effective management focuses on reducing symptoms and preventing disease progression through lifestyle modifications, medications, and interventional procedures. Timely diagnosis and treatment are crucial for enhancing patient quality of life. Designing and developing experimental drugs is challenging and costly, which makes mathematical and computational methods essential for efficient drug discovery. In this article, we introduce a novel molecular descriptor based on a graph theory-driven degree partitioning technique, integrated into a quantitative structure-property relationships (QSPR) framework. Using quadratic regression, we determine the optimal predictors for four key properties boiling point, enthalpy of vaporization, flash point, and index of refraction for sixteen anti-angina drugs based on nine degree-based topological indices. Furthermore, by combining these descriptors with the multi-attribute decision-making additive ratio assessment technique, we achieve robust and reliable drug rankings. Our innovative integration of a new molecular descriptor with advanced statistical and decision-making methods not only improves predictive accuracy but also provides a novel and efficient approach for the development and optimization of angina drug therapies.

Keywords: ARASS method; Angina disease; Molecular graph; QSPR analysis; Regression model.

Fig. 1

Chemical structures of the drugs that help fight angina disease.

Fig. 2

Ranking of angina treatment medications via ARASS.

Fig. 3

Graphs of the correlation coefficient of the linear regression model.

Fig. 4

Graphs of the correlation coefficient of the quadratic regression model.

Fig. 5

Bar chart comparing the number of descriptors across different molecular descriptor sets.