Empowering natural product science with AI: leveraging multimodal data and knowledge graphs
Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the applicatio...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Royal Society of Chemistry
2025
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| Online Access: | https://hdl.handle.net/1721.1/158163 |
| _version_ | 1824458294747463680 |
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| author | Meijer, David Beniddir, Mehdi A Coley, Connor W Mejri, Yassine M Öztürk, Meltem van der Hooft, Justin JJ Medema, Marnix H Skiredj, Adam |
| author2 | Massachusetts Institute of Technology. Department of Chemical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Chemical Engineering Meijer, David Beniddir, Mehdi A Coley, Connor W Mejri, Yassine M Öztürk, Meltem van der Hooft, Justin JJ Medema, Marnix H Skiredj, Adam |
| author_sort | Meijer, David |
| collection | MIT |
| description | Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the application of AI to emulate human cognition in natural product research and its subsequent impact has so far been limited. One reason for this limited impact is that available natural product data is multimodal, unbalanced, unstandardized, and scattered across many data repositories. This makes natural product data challenging to use with existing deep learning architectures that consume fairly standardized, often non-relational, data. It also prevents models from learning overarching patterns in natural product science. In this Viewpoint, we address this challenge and support ongoing initiatives aimed at democratizing natural product data by collating our collective knowledge into a knowledge graph. By doing so, we believe there will be an opportunity to use such a knowledge graph to develop AI models that can truly mimic natural product scientists' decision-making. |
| first_indexed | 2025-02-19T04:23:37Z |
| format | Article |
| id | mit-1721.1/158163 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:23:37Z |
| publishDate | 2025 |
| publisher | Royal Society of Chemistry |
| record_format | dspace |
| spelling | mit-1721.1/1581632025-02-03T20:49:10Z Empowering natural product science with AI: leveraging multimodal data and knowledge graphs Meijer, David Beniddir, Mehdi A Coley, Connor W Mejri, Yassine M Öztürk, Meltem van der Hooft, Justin JJ Medema, Marnix H Skiredj, Adam Massachusetts Institute of Technology. Department of Chemical Engineering Artificial intelligence (AI) is accelerating how we conduct science, from folding proteins with AlphaFold and summarizing literature findings with large language models, to annotating genomes and prioritizing newly generated molecules for screening using specialized software. However, the application of AI to emulate human cognition in natural product research and its subsequent impact has so far been limited. One reason for this limited impact is that available natural product data is multimodal, unbalanced, unstandardized, and scattered across many data repositories. This makes natural product data challenging to use with existing deep learning architectures that consume fairly standardized, often non-relational, data. It also prevents models from learning overarching patterns in natural product science. In this Viewpoint, we address this challenge and support ongoing initiatives aimed at democratizing natural product data by collating our collective knowledge into a knowledge graph. By doing so, we believe there will be an opportunity to use such a knowledge graph to develop AI models that can truly mimic natural product scientists' decision-making. 2025-02-03T20:49:08Z 2025-02-03T20:49:08Z 2024-08-16 2025-02-03T20:42:30Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/158163 Meijer, David, Beniddir, Mehdi A, Coley, Connor W, Mejri, Yassine M, Öztürk, Meltem et al. 2024. "Empowering natural product science with AI: leveraging multimodal data and knowledge graphs." Natural Product Reports. en 10.1039/d4np00008k Natural Product Reports Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Royal Society of Chemistry Royal Society of Chemistry |
| spellingShingle | Meijer, David Beniddir, Mehdi A Coley, Connor W Mejri, Yassine M Öztürk, Meltem van der Hooft, Justin JJ Medema, Marnix H Skiredj, Adam Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title | Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title_full | Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title_fullStr | Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title_full_unstemmed | Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title_short | Empowering natural product science with AI: leveraging multimodal data and knowledge graphs |
| title_sort | empowering natural product science with ai leveraging multimodal data and knowledge graphs |
| url | https://hdl.handle.net/1721.1/158163 |
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