QSPR analysis of distance-based structural indices for drug compounds in tuberculosis treatment
Micheal Arockiaraj,
Francis Joseph H. Campena,
A. Berin Greeni,
Muhammad Usman Ghani,
S. Gajavalli,
Fairouz Tchier,
Ahmad Zubair Jan
Affiliations
Micheal Arockiaraj
Department of Mathematics, Loyola College, Chennai 600034, India
Francis Joseph H. Campena
Department of Mathematics and Statistics, College of Science, De La Salle University, 2401 Taft Avenue, Malate, Manila, 1004 Metro Manila, Philippines
A. Berin Greeni
School of Advanced Sciences, Vellore Institute of Technology, Chennai 600127, India
Muhammad Usman Ghani
Institute of Mathematics, Khawaja Fareed University of Engineering Information Technology, Abu Dhabi Road, 64200, Rahim Yar Khan, Pakistan; Corresponding author.
S. Gajavalli
School of Advanced Sciences, Vellore Institute of Technology, Chennai 600127, India
Fairouz Tchier
Mathematics Department, King Saudi University, Riyadh, 145111, Saudi Arabia
Ahmad Zubair Jan
Wroclaw University of Science and Technology, Faculty of Mechanical Engineering, Poland
Tuberculosis (TB) is one of the most contagious diseases that has a greater mortality rate than HIV/AIDS and the cases of TB are feared to rise as a repercussion of the COVID-19 pandemic. The pharmaceutical industry is constantly looking for ways to improve drug design processes in order to combat the growth of infections and cure newly identified syndromes or genetically based dysfunctions with the help of QSPR models. QSPR models are mathematical tools that establish relationships between a molecular structure and its physicochemical attributes using structural properties. Topological indices are such properties that are generated from the molecular graph without any empirically derived measurements. This work focuses on developing a QSPR model using distance-based topological indices for anti-tuberculosis medications and their diverse physicochemical features.
