Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models
Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-p...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-024-01393-y |
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| _version_ | 1832594891037736960 |
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| author | Geemi P. Wellawatte Philippe Schwaller |
| author_facet | Geemi P. Wellawatte Philippe Schwaller |
| author_sort | Geemi P. Wellawatte |
| collection | DOAJ |
| description | Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations. |
| format | Article |
| id | doaj-art-34767decf93b4780ad7634c167887484 |
| institution | Kabale University |
| issn | 2399-3669 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Chemistry |
| spelling | doaj-art-34767decf93b4780ad7634c1678874842025-01-19T12:13:13ZengNature PortfolioCommunications Chemistry2399-36692025-01-018111010.1038/s42004-024-01393-yHuman interpretable structure-property relationships in chemistry using explainable machine learning and large language modelsGeemi P. Wellawatte0Philippe Schwaller1Laboratory of Artificial Chemical Intelligence, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)Laboratory of Artificial Chemical Intelligence, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL)Abstract Explainable Artificial Intelligence (XAI) is an emerging field in AI that aims to address the opaque nature of machine learning models. Furthermore, it has been shown that XAI can be used to extract input-output relationships, making them a useful tool in chemistry to understand structure-property relationships. However, one of the main limitations of XAI methods is that they are developed for technically oriented users. We propose the XpertAI framework that integrates XAI methods with large language models (LLMs) accessing scientific literature to generate accessible natural language explanations of raw chemical data automatically. We conducted 5 case studies to evaluate the performance of XpertAI. Our results show that XpertAI combines the strengths of LLMs and XAI tools in generating specific, scientific, and interpretable explanations.https://doi.org/10.1038/s42004-024-01393-y |
| spellingShingle | Geemi P. Wellawatte Philippe Schwaller Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models Communications Chemistry |
| title | Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models |
| title_full | Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models |
| title_fullStr | Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models |
| title_full_unstemmed | Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models |
| title_short | Human interpretable structure-property relationships in chemistry using explainable machine learning and large language models |
| title_sort | human interpretable structure property relationships in chemistry using explainable machine learning and large language models |
| url | https://doi.org/10.1038/s42004-024-01393-y |
| work_keys_str_mv | AT geemipwellawatte humaninterpretablestructurepropertyrelationshipsinchemistryusingexplainablemachinelearningandlargelanguagemodels AT philippeschwaller humaninterpretablestructurepropertyrelationshipsinchemistryusingexplainablemachinelearningandlargelanguagemodels |