CombinatorixPy: Advancing mixture descriptors for computational chemistry
Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) is a machine learning approach to predict chemical and physical properties of pure compounds; however, it has limited application in multi-component compounds. The complex and layered nature of multi-component materials presents chall...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000275 |
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| author | Rahil Ashtari Mahini Gerardo Casanola-Martin Stephen Szwiec Simone A. Ludwig Bakhtiyor Rasulev |
| author_facet | Rahil Ashtari Mahini Gerardo Casanola-Martin Stephen Szwiec Simone A. Ludwig Bakhtiyor Rasulev |
| author_sort | Rahil Ashtari Mahini |
| collection | DOAJ |
| description | Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) is a machine learning approach to predict chemical and physical properties of pure compounds; however, it has limited application in multi-component compounds. The complex and layered nature of multi-component materials presents challenges in computing molecular representation, thus limiting the application of QSAR and QSPR. In this study, a new method has been proposed to derive numerical representation based on a combinatorial approach. It calculates all the possible interactions between different components in reaction using the Cartesian product over sets of descriptors of constituents, considering each multi-component material as a mixture system. A Python package was developed to calculate mixture descriptors based on this arithmetic equation, which can be used in machine learning-based QSAR and QSPR models. |
| format | Article |
| id | doaj-art-155700d4f07640468aa73d07f26376d1 |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-155700d4f07640468aa73d07f26376d12025-02-02T05:27:43ZengElsevierSoftwareX2352-71102025-02-0129102060CombinatorixPy: Advancing mixture descriptors for computational chemistryRahil Ashtari Mahini0Gerardo Casanola-Martin1Stephen Szwiec2Simone A. Ludwig3Bakhtiyor Rasulev4Department of Computer Science, North Dakota State University, 1320 Albrecht Boulevard, Fargo, ND 58105, United States of America; Department of Coatings and Polymeric Materials, North Dakota State University, 1735 NDSU Research Park, Drive N, Fargo, ND 58102, United States of AmericaDepartment of Coatings and Polymeric Materials, North Dakota State University, 1735 NDSU Research Park, Drive N, Fargo, ND 58102, United States of AmericaCenter for Computationally Assisted Science and Technology, North Dakota State University, 1805 NDSU Research Park, Drive N, Fargo, ND, 58102, United States of America; Materials and Nanotechnology Program, North Dakota State University, Fargo, ND 58108, United States of AmericaDepartment of Computer Science, North Dakota State University, 1320 Albrecht Boulevard, Fargo, ND 58105, United States of America; Corresponding authors.Department of Coatings and Polymeric Materials, North Dakota State University, 1735 NDSU Research Park, Drive N, Fargo, ND 58102, United States of America; Corresponding authors.Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) is a machine learning approach to predict chemical and physical properties of pure compounds; however, it has limited application in multi-component compounds. The complex and layered nature of multi-component materials presents challenges in computing molecular representation, thus limiting the application of QSAR and QSPR. In this study, a new method has been proposed to derive numerical representation based on a combinatorial approach. It calculates all the possible interactions between different components in reaction using the Cartesian product over sets of descriptors of constituents, considering each multi-component material as a mixture system. A Python package was developed to calculate mixture descriptors based on this arithmetic equation, which can be used in machine learning-based QSAR and QSPR models.http://www.sciencedirect.com/science/article/pii/S2352711025000275Computational chemistryMixture-based QSARMixture-based QSPRMixture descriptorCombinatorial DescriptorCartesian product |
| spellingShingle | Rahil Ashtari Mahini Gerardo Casanola-Martin Stephen Szwiec Simone A. Ludwig Bakhtiyor Rasulev CombinatorixPy: Advancing mixture descriptors for computational chemistry SoftwareX Computational chemistry Mixture-based QSAR Mixture-based QSPR Mixture descriptor Combinatorial Descriptor Cartesian product |
| title | CombinatorixPy: Advancing mixture descriptors for computational chemistry |
| title_full | CombinatorixPy: Advancing mixture descriptors for computational chemistry |
| title_fullStr | CombinatorixPy: Advancing mixture descriptors for computational chemistry |
| title_full_unstemmed | CombinatorixPy: Advancing mixture descriptors for computational chemistry |
| title_short | CombinatorixPy: Advancing mixture descriptors for computational chemistry |
| title_sort | combinatorixpy advancing mixture descriptors for computational chemistry |
| topic | Computational chemistry Mixture-based QSAR Mixture-based QSPR Mixture descriptor Combinatorial Descriptor Cartesian product |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025000275 |
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