Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.

Chemical graphs are mathematical representations of molecular structures, where atoms are represented as vertices, while chemical bonds are depicted as edges of a graph. The chemical graphs are widely used in cheminformatics to analyze molecular properties, predict biological activity and design new drugs. A topological index (TI) in drug design is a numerical descriptor of a molecular graph that correlates its structure with biological activity and physicochemical properties. The aim of this study is to use the concepts of chemical graphs to examine the molecular characteristics and structural design of anti-HIV drugs. Secondly, we have utilized the concept of supervised machine learning to create a predictive model. Finally, we have compared the results of different machine learning algorithms such as Random Forest algorithm and XGBoost algorithm. These methods not only enhance drug effectiveness but also aid in predicting new drug candidates.