Abstract

Sreeja S.*, Anjana V. S., Ajna S. A., Jefna Jafar

ABSTRACT

Cardiac glycosides are a class of naturally derived compounds with potent therapeutic effects, particularly in the treatment of heart failure and arrhythmias. However, their narrow therapeutic index and potential toxicity necessitate the development of safer, more effective analogues. This study presents an integrated approach combining structural analysis, computational design, and AI-assisted synthesis to innovate and optimize cardiac glycoside analogues. Detailed structural elucidation through techniques such as X-ray crystallography, NMR spectroscopy, and molecular docking provides insights into key pharmacophores and structure–activity relationships. Computational modeling and in silico screening, including molecular dynamics simulations and QSAR analyses, guide the rational design of analogues with improved binding affinity and reduced off-target effects. Artificial intelligence tools are employed to predict synthetic pathways, optimize reaction conditions, and accelerate lead compound generation. This multidisciplinary strategy enhances the efficiency of drug discovery, reduces development time, and increases the likelihood of identifying novel analogues with better therapeutic profiles. The integrated platform outlined in this work holds promise for advancing cardiac glycoside research and developing next-generation cardiovascular therapeutics.