Molecular graphs and entropy based QSPR analysis of drugs by using machine learning
Wakeel Ahmed, Tehseen Ashraf, Shahid Zaman, Kashif Ali, Ali Hussain, Melaku Berhe Belay
Abstract
This research article explores the significance of molecular characteristics and structural design of sulfur $$(S^{VI})$$ -based drugs. Topological descriptors and entropy of these drugs were calculated for analyzing the structural properties, in order to enhance our understanding of molecular behavior. Furthermore, their physicochemical properties were explored by utilizing supervised machine learning algorithms and performing Quantitative Structure-Property Relationship (QSPR) analysis, which explains the connections between the topological descriptor and physicochemical attributes. This comprehensive approach elucidates the molecular characteristics of sulfur $$(S^{VI})$$ -based drugs, establishing the foundation for a deeper understanding of their pharmacological effects and therapeutic capacity.