Byung Tae Lee , Byeongsub Lee , Joon Young Kwon , Tilmann Weber , Hyun Uk Kim
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引用次数: 0
Abstract
Covering: 2020 to 2025
Natural 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.
期刊介绍:
Natural Product Reports (NPR) serves as a pivotal critical review journal propelling advancements in all facets of natural products research, encompassing isolation, structural and stereochemical determination, biosynthesis, biological activity, and synthesis.
With a broad scope, NPR extends its influence into the wider bioinorganic, bioorganic, and chemical biology communities. Covering areas such as enzymology, nucleic acids, genetics, chemical ecology, carbohydrates, primary and secondary metabolism, and analytical techniques, the journal provides insightful articles focusing on key developments shaping the field, rather than offering exhaustive overviews of all results.
NPR encourages authors to infuse their perspectives on developments, trends, and future directions, fostering a dynamic exchange of ideas within the natural products research community.