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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000275 |
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| Summary: | 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. |
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| ISSN: | 2352-7110 |