QSPR Analysis of Diverse Drugs Using Linear Regression for Predicting Physical Properties

Abstract

The study focused on QSPR (Quantitative Structure-Property Relationship) analysis using a variety of topological indices on the medications Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Through the use of computational modeling approaches, the study sought to determine how these medications’ chemical structures relate to their individual qualities. The discovered results provided information on the quantitative correlations between structural characteristics and pharmacological qualities, allowing for better comprehension and forecast of their behavior. The results of this study make a positive contribution to the field of medication discovery and design by offering important knowledge about the structure-property correlations of these medicinal molecules. In this article, we focus on using topological indices and a linear regression model to successfully predict various pharmacological features. This method enables more effective drug discovery and development by providing insights into the connection between molecular structure and pharmacological characteristics. We can improve our comprehension of drug behavior and assist targeted drug design by utilizing topological indices and regression analysis.

Keywords:

• Degree-based indices
• QSPR
• degree of vertex
• linear regression

Disclosure statement

No potential conflict of interest was reported by the authors.

Authors’ contributions

This work was equally contributed by all writers.

Data availability

The data used to support the findings of this study are cited at relevant places within the text as references.