Predictive Modelling and Ranking: Azadirachta Indica Compounds through Indices and Multi-Criteria Decision-Making Techniques

Anuradha D SJaganathan Baladasan^*

• Vellore Institute of Technology (VIT), Chennai, India

Azadirachta indica, commonly known as neem has demonstrated medicinal potential against various chronic diseases. However, their clinical translation remains challenging because of the biodiversity and complexity of the drug development process. This study aimed to analyze neem compounds using topological indices (TIs) to predict their physicochemical properties. Valency-based indices, including Zagreb and atom bond connectivity indices, were employed to characterize the boiling point, vaporization, enthalpy, mass, and refractivity. Regression analysis and multi-criteria decision-making methods were used for predictive modeling and compound ranking. Statistical metrics demonstrated thepredictive power of the models, whereas ranking methods provided a hierarchical ordering of compounds based on their therapeutic potential. This study contributes to the analogous prediction, optimization, and virtual screening of neem compounds using a cost-effective approach, potentially accelerating drug discovery. The findings of this study offer insight into the properties of neem compounds, paving the way for more efficient drug development.