Article Dans Une Revue Natural Product Reports Année : 2024

Empowering natural product science with AI: leveraging multimodal data and knowledge graphs

Résumé

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 nonrelational, 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.

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hal-04700044 , version 1 (17-09-2024)

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David Meijer, Mehdi A Beniddir, Connor W Coley, Yassine M Mejri, Meltem Öztürk, et al.. Empowering natural product science with AI: leveraging multimodal data and knowledge graphs. Natural Product Reports, In press, ⟨10.1039/d4np00008k⟩. ⟨hal-04700044⟩
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