QSPR-Based Statistical Study of Uphill Topological Indices for Anti-Tuberculosis Drugs

Abstract

Tuberculosis (TB) is still one of the most infectious and lethal diseases in the world, with mortality rates surpassing even HIV/AIDS. In response to the urgent need for more effective treatments, the pharmaceutical industry is increasingly turning to Quantitative Structure-Property Relationship (QSPR) models. These models play an important role in drug discovery and development because they allow for the design of compounds with specific biological activities. Researchers hope to use QSPR techniques to better control the spread of tuberculosis and combat emerging syndromes and genetic disorders. This study aims to develop a QSPR model for tuberculosis medications utilizing six physicochemical properties through Uphill degree-based topological indices. Our research and predictive models have the potential to make a significant contribution to the development of novel tuberculosis treatments.