MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling
Multi-component materials/compounds and polymeric/composite systems pose structural complexity that challenges the conventional methods of molecular representation in cheminformatics, which have limited applicability in such cases. Therefore, we have introduced an innovative structural representatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | SoftwareX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024002814 |
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| author | Rahil Ashtari Mahini Gerardo Casanola-Martin Simone A. Ludwig Bakhtiyor Rasulev |
| author_facet | Rahil Ashtari Mahini Gerardo Casanola-Martin Simone A. Ludwig Bakhtiyor Rasulev |
| author_sort | Rahil Ashtari Mahini |
| collection | DOAJ |
| description | Multi-component materials/compounds and polymeric/composite systems pose structural complexity that challenges the conventional methods of molecular representation in cheminformatics, which have limited applicability in such cases. Therefore, we have introduced an innovative structural representation technique tailored for complex materials. We implemented different mixing rules based on linear and nonlinear relationships’ additive effect of different components in composites treating each multi-component material as a mixture system. We developed and improved mixture descriptors based on 12 different mixture functions grouped into three main categories: property-based descriptors, concentration-weighted descriptors, and deviation-combination descriptors. A python package was developed for this purpose, allowing users to compute 12 different mixture-descriptors to use as input for the generation of mixture-based Quantitative Structure-Activity/Property Relationship (mxb-QSAR/QSPR) machine learning models for predicting a range of chemical and physical properties across various complex systems. |
| format | Article |
| id | doaj-art-9d720b437a394cf883b5b3a68d84b4ce |
| institution | OA Journals |
| issn | 2352-7110 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-9d720b437a394cf883b5b3a68d84b4ce2025-08-20T02:07:01ZengElsevierSoftwareX2352-71102024-12-012810191110.1016/j.softx.2024.101911MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modelingRahil Ashtari Mahini0Gerardo Casanola-Martin1Simone A. Ludwig2Bakhtiyor Rasulev3Department 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 AmericaDepartment of Computer Science, North Dakota State University, 1320 Albrecht Boulevard, Fargo, ND 58105, 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 America; Corresponding author.Multi-component materials/compounds and polymeric/composite systems pose structural complexity that challenges the conventional methods of molecular representation in cheminformatics, which have limited applicability in such cases. Therefore, we have introduced an innovative structural representation technique tailored for complex materials. We implemented different mixing rules based on linear and nonlinear relationships’ additive effect of different components in composites treating each multi-component material as a mixture system. We developed and improved mixture descriptors based on 12 different mixture functions grouped into three main categories: property-based descriptors, concentration-weighted descriptors, and deviation-combination descriptors. A python package was developed for this purpose, allowing users to compute 12 different mixture-descriptors to use as input for the generation of mixture-based Quantitative Structure-Activity/Property Relationship (mxb-QSAR/QSPR) machine learning models for predicting a range of chemical and physical properties across various complex systems.http://www.sciencedirect.com/science/article/pii/S2352711024002814CheminformaticsMixture-based QSARMixture-based QSPRMixture descriptorMixing ruleMixtureMetrics |
| spellingShingle | Rahil Ashtari Mahini Gerardo Casanola-Martin Simone A. Ludwig Bakhtiyor Rasulev MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling SoftwareX Cheminformatics Mixture-based QSAR Mixture-based QSPR Mixture descriptor Mixing rule MixtureMetrics |
| title | MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| title_full | MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| title_fullStr | MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| title_full_unstemmed | MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| title_short | MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| title_sort | mixturemetrics a comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling |
| topic | Cheminformatics Mixture-based QSAR Mixture-based QSPR Mixture descriptor Mixing rule MixtureMetrics |
| url | http://www.sciencedirect.com/science/article/pii/S2352711024002814 |
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