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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI and mechanistic modeling for characterizing biosynthetic pathways of natural products

Hyun Uk Kim, Byeongsub Lee, Byung Tae Lee, Tilmann Weber et al.
1 citations
Natural Product Reports
Plant biochemistry and biosynthesis
0.81
article

AI and mechanistic modeling for characterizing biosynthetic pathways of natural products

Hyun Uk Kim, Byeongsub Lee, Byung Tae Lee, Tilmann Weber, Jin‐Kyung Kwon
article en
1 citations

Abstract

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.

Natural Product Reports
Korea Advanced Institute of Science and Technology (KR), Novo Nordisk Foundation (DK), Technical University of Denmark (DK)
Openalex Percentile: Top 22%
Plant biochemistry and biosynthesis
0.81
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.