Abstract
Duchenne muscular dystrophy is a critical, progressively worsening, and ultimately deadly illness characterized by the deterioration of skeletal muscles, respiratory failure, and heart disease. The pharmaceutical industries are persistently innovating drug design processes to address the rise of infections and effectively treat emerging syndromes or genetically based disorders with the help of quantitative structure-property relationship models. These models are mathematical tools that correlate molecular structures with their physicochemical properties through structural characteristics. Different models can be generated based on the various structural features of the compounds, and topological indices are one such significant structural feature generated from the molecular graph and are key tools used in these models. This study focuses on creating quantitative structure-property relationship models using degree-based topological indices, which are highly effective in quantitative structure-property relationship analysis to explore the diverse physicochemical properties of Duchenne muscular dystrophy drugs with the prediction of properties of a recently approved drug givinostat. Furthermore, the drug discovery and development activities can be accelerated using the developed models to forecast the possible productiveness of novel Duchenne muscular dystrophy treatment drugs.