Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis
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 m...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-99583-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850206299315240960 |
|---|---|
| author | YuLan Chen Abdul Rauf Aqsa Shafique Fairouz Tchier Adnan Aslam Keneni Abera Tola |
| author_facet | YuLan Chen Abdul Rauf Aqsa Shafique Fairouz Tchier Adnan Aslam Keneni Abera Tola |
| author_sort | YuLan Chen |
| collection | DOAJ |
| description | 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 2 < 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. |
| format | Article |
| id | doaj-art-690f7dd5b1674dfab9647f53e921109e |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-690f7dd5b1674dfab9647f53e921109e2025-08-20T02:10:53ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-99583-8Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysisYuLan Chen0Abdul Rauf1Aqsa Shafique2Fairouz Tchier3Adnan Aslam4Keneni Abera Tola5Department of Neurology, Nanjing Pukou Hospital of TCMDepartment of Mathematics, Air University Multan CampusDepartment of Mathematics, Air University Multan CampusMathematics Department, College of Science, King Saud UniversityDepartment of Natural Sciences and Humanities, University of Engineering and Technology Lahore RCETDepartment of Mathematics, College of Natural and Computational Science, Dambi Dollo UniversityAbstract 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 2 < 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.https://doi.org/10.1038/s41598-025-99583-8Fuzzy TopsisTopological descriptorsBiochemistryHuman healthLinear regression |
| spellingShingle | YuLan Chen Abdul Rauf Aqsa Shafique Fairouz Tchier Adnan Aslam Keneni Abera Tola Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis Scientific Reports Fuzzy Topsis Topological descriptors Biochemistry Human health Linear regression |
| title | Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis |
| title_full | Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis |
| title_fullStr | Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis |
| title_full_unstemmed | Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis |
| title_short | Physicochemical profiling and ranking of parkinson’s disease drugs through QSPR and Fuzzy TOPSIS analysis |
| title_sort | physicochemical profiling and ranking of parkinson s disease drugs through qspr and fuzzy topsis analysis |
| topic | Fuzzy Topsis Topological descriptors Biochemistry Human health Linear regression |
| url | https://doi.org/10.1038/s41598-025-99583-8 |
| work_keys_str_mv | AT yulanchen physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis AT abdulrauf physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis AT aqsashafique physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis AT fairouztchier physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis AT adnanaslam physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis AT keneniaberatola physicochemicalprofilingandrankingofparkinsonsdiseasedrugsthroughqsprandfuzzytopsisanalysis |