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Updated: May 16, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predictive modelling and ranking: Azadirachta indica compounds through indices and multi-criteria decision-making
1Department of Mathematics, Vellore Institute of Technology, Chennai, India.
Introduction:
Azadirachta indica (neem) shows medicinal potential against chronic diseases, but clinical translation is challenging. This study aimed to analyze neem compounds using topological indices (TIs) to predict physicochemical properties.
Methods:
Valency-based indices, including Zagreb and atom bond connectivity indices, were used to characterize boiling point, vaporization, enthalpy, mass, and refractivity. Regression analysis and multi-criteria decision-making methods were employed for predictive modeling and compound ranking.
Results:
Statistical metrics demonstrated the predictive power of the models. Ranking methods provided a hierarchical ordering of compounds based on therapeutic potential.
Discussion:
This study contributes to analogous prediction, optimization, and virtual screening of neem compounds using a cost-effective approach. The findings offer insight into neem compound properties, potentially accelerating drug discovery and development.
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