计算生物学
自然(考古学)
计算机科学
生化工程
计算模型
钥匙(锁)
药物发现
机制(生物学)
数据科学
化学
合成生物学
通路分析
代谢途径
天然产物
化学信息学
系统生物学
生物
药物靶点
铅(地质)
作者
Byung Tae Lee,Byeongsub Lee,Jin‐Kyung Kwon,Tilmann Weber,Hyun Uk Kim
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI