AI and mechanistic modeling for characterizing biosynthetic pathways of natural products
Covering: 2020 to 2025Natural products are a major source of bioactive compounds, yet elucidating their biosynthetic pathways remains a major challenge due to complex genotype-phenotype relationships. Recent advances in computational approaches, particularly artificial intelligence (AI) and mechanistic modeling, are transforming this field. This highlight examines key databases that underpin computational studies, AI-driven methods for predicting biosynthetic pathways and enzyme-substrate interactions, and mechanistic simulations that provide energetic and structural insights. We also discuss current challenges and future opportunities for integrating these strategies to accelerate discovery, engineering, and application of natural products in drug discovery, biotechnology, and synthetic biology.
Authors
- Hyun Uk Kim (ORCID: https://orcid.org/0000-0001-7224-642X)
- Byeongsub Lee (ORCID: https://orcid.org/0009-0004-6837-4719)
- Byung Tae Lee (ORCID: https://orcid.org/0009-0008-3623-3734)
- Tilmann Weber (ORCID: https://orcid.org/0000-0002-8260-5120)
- Jin‐Kyung Kwon (ORCID: https://orcid.org/0009-0006-3138-589X)
Institutions
- Korea Advanced Institute of Science and Technology (KR)
- Novo Nordisk Foundation (DK)
- Technical University of Denmark (DK)
Publication Details
- Journal
- Natural Product Reports
- Published
- 2025-11-07
- DOI
- https://doi.org/10.1039/d5np00059a
- Citations
- 1
- Primary Topic
- Plant biochemistry and biosynthesis
- Type
- article
- Field-Weighted Citation Impact
- 0.81