A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis
Abstract Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topologic...
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BMC
2025-01-01
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| Series: | BMC Chemistry |
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| Online Access: | https://doi.org/10.1186/s13065-024-01374-1 |
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| author | Fozia Bashir Farooq Nazeran Idrees Esha Noor Nouf Abdulrahman Alqahtani Muhammad Imran |
| author_facet | Fozia Bashir Farooq Nazeran Idrees Esha Noor Nouf Abdulrahman Alqahtani Muhammad Imran |
| author_sort | Fozia Bashir Farooq |
| collection | DOAJ |
| description | Abstract Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS. Using a linear regression approach, we create models for Quantitative Structure-Property Relations (QSPR), detecting correlations between properties such as enthalpy of vaporization, flash point, molar weight, polarizability, molar volume, and complexity with certain degree related topological indices. We used a dataset related to drugs for MS with known properties for training the model and also for validation. To prioritize the most promising drug candidates, we used multi-criteria decision making based on the predicted properties and topological indices, allowing for more informed decisions. The 12 drug candidates were prioritized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and two Weighted Aggregated Sum Product Assessment (WASPAS) methods. The rankings obtained using TOPSIS, WASPAS methods showed a high level of agreement among the results. This framework can be broadly applied to rationally design new therapeutics for complex diseases. |
| format | Article |
| id | doaj-art-c57335286d3d4e25925b647e65838293 |
| institution | Kabale University |
| issn | 2661-801X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Chemistry |
| spelling | doaj-art-c57335286d3d4e25925b647e658382932025-01-05T12:07:31ZengBMCBMC Chemistry2661-801X2025-01-0119111410.1186/s13065-024-01374-1A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysisFozia Bashir Farooq0Nazeran Idrees1Esha Noor2Nouf Abdulrahman Alqahtani3Muhammad Imran4Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics, Government College University FaisalabadDepartment of Mathematics, Government College University FaisalabadDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Electrical Engineering, Prince Mohammad Bin Fahd UniversityAbstract Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS. Using a linear regression approach, we create models for Quantitative Structure-Property Relations (QSPR), detecting correlations between properties such as enthalpy of vaporization, flash point, molar weight, polarizability, molar volume, and complexity with certain degree related topological indices. We used a dataset related to drugs for MS with known properties for training the model and also for validation. To prioritize the most promising drug candidates, we used multi-criteria decision making based on the predicted properties and topological indices, allowing for more informed decisions. The 12 drug candidates were prioritized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and two Weighted Aggregated Sum Product Assessment (WASPAS) methods. The rankings obtained using TOPSIS, WASPAS methods showed a high level of agreement among the results. This framework can be broadly applied to rationally design new therapeutics for complex diseases.https://doi.org/10.1186/s13065-024-01374-1Topological indexDecision-makingChemical graphsCorrelation |
| spellingShingle | Fozia Bashir Farooq Nazeran Idrees Esha Noor Nouf Abdulrahman Alqahtani Muhammad Imran A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis BMC Chemistry Topological index Decision-making Chemical graphs Correlation |
| title | A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis |
| title_full | A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis |
| title_fullStr | A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis |
| title_full_unstemmed | A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis |
| title_short | A computational approach to drug design for multiple sclerosis via QSPR modeling, chemical graph theory, and multi-criteria decision analysis |
| title_sort | computational approach to drug design for multiple sclerosis via qspr modeling chemical graph theory and multi criteria decision analysis |
| topic | Topological index Decision-making Chemical graphs Correlation |
| url | https://doi.org/10.1186/s13065-024-01374-1 |
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