Physicochemical profiling and ranking of parkinson's disease drugs through QSPR and Fuzzy TOPSIS analysis

Parkinson's disease is a progressive neurological disorder characterized by the degeneration of the nervous system, leading to impaired motor and non-motor functions. Early symptoms include tremors, rigidity, and bradykinesia, with progressive deterioration over time. This study employs a multi-criteria decision-making approach, integrating Fuzzy TOPSIS and Quantitative Structure-Property Relationship (QSPR) analysis, to evaluate and rank 17 Parkinson's disease medications based on their physicochemical properties. Molecular structures were encoded as adjacency matrices using MATLAB 2017, and six Sombor index variants-computed via a custom Maple 2020 algorithm-served as topological descriptors for QSPR modeling. Eight critical physicochemical properties were analyzed: polarizability (P), boiling point (BP), surface tension (ST), polar surface area (PSA), flash point (FP), molar refractivity (MR), enthalpy of vaporization (EV), and molar volume (MV). The Fuzzy TOPSIS ranking revealed bromocriptine as the top-performing drug for boiling point (BP), while comparative rankings across all properties are tabulated for clinical reference. Validation metrics, including coefficient of determination, mean squared error , and mean absolute error, confirmed model robustness. Notably, surface tension (ST) and polar surface area (PSA) showed weaker correlations (R² < 0.5, p > 0.05), highlighting limitations in their predictability via Sombor indices. This work demonstrates the utility of combining chemical graph theory, QSPR modeling, and Fuzzy TOPSIS for rational drug evaluation in neurodegenerative disorders. The methodology offers a framework for prioritizing therapeutics based on physicochemical profiles, with implications for optimizing Parkinson's disease management.