Multi-criteria decision analysis of dendrimer applications in drug delivery systems

Dendrimers are highly branched, well-defined macromolecules characterized by exceptional structural precision and functional adaptability. These properties render dendrimers valuable in nanotechnology, drug delivery, and catalysis. However, selecting the most suitable dendrimer for drug delivery is difficult because its branching pattern, structural, and physicochemical properties are closely related. In this research, we utilized the multi-criteria decision-making (MCDM) technique to identify potential candidates for drug delivery applications among the four considered families: phosphorus-containing, PDI-cored, Triazine-based, and Aliphatic polyamide dendrimers. Both qualitative and quantitative molecular descriptors are integrated within the model. Degree-based topological indices, including the Narumi Katayama (NK), Modified Narumi Katayama (NK $_{}^{\ast }$ ), Forgotten Index (F-index) and Augmented Zagreb Index (AZI) indices, are utilized to capture variations in molecular complexity, stability, and branching efficiency. The evaluation was conducted using entropy-based weights obtained from representative benzene derivatives; also, the equal weighting and sensitivity analyses were utilized to validate the ranking results. This study uses multi-objective optimization on the basis of ratio analysis (MOORA), simple additive weighting (SAW), and the technique for order preference by similarity to ideal solution (TOPSIS) techniques for criteria weighting, ranking, and reflects how topological descriptors support decision-making in dendrimer selection. The results of these investigations provide quantitative guidance for chemists and pharmaceutical researchers on the forecasting capacity of topological indices in designing dendrimers with enhanced drug delivery.