Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms

被引:25
|
作者
Li, Gang [1 ]
Lin, Ping [1 ]
Wang, Ke [1 ]
Gu, Chen-Chen [1 ]
Kusari, Souvik [2 ]
机构
[1] Qingdao Univ, Sch Pharm, Dept Nat Med Chem & Pharmacognosy, Qingdao 266071, Peoples R China
[2] Tech Univ Dortmund, Fac Chem & Chem Biol, Ctr Mass Spectrometry, D-44227 Dortmund, Germany
基金
中国国家自然科学基金;
关键词
MICROBIAL NATURAL-PRODUCTS; MOLECULAR NETWORKING; MACROMOLECULAR TARGETS; RAPID DEREPLICATION; MASS-SPECTROMETRY; PREDICTION; IDENTIFICATION; ANNOTATION; ENDOPHYTES; DATABASE;
D O I
10.1016/j.trecan.2021.10.002
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.
引用
收藏
页码:65 / 80
页数:16
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