ArticleScientific reports2025
Physicochemical profiling and ranking of parkinson's disease drugs through QSPR and Fuzzy TOPSIS analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- QSPR analysis of the drugs used to treat renal failure and its complications using degree and modified reverse degree indices.Scientific reports · 2026Article
- QSPR modelling of PPI drugs using hybrid topological indices.Frontiers in chemistry · 2026Article
- QSPR analysis of the drugs used to treat major depressive disorder using degree and neighborhood degree based indices.Frontiers in chemistry · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (R
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