药物发现
铅(地质)
天然产物
计算机科学
计算生物学
生化工程
生物技术
数据科学
生物
生物信息学
工程类
生物化学
古生物学
作者
Gang Li,Ping Lin,Ke Wang,Chen-Chen Gu,Souvik Kusari
标识
DOI:10.1016/j.trecan.2021.10.002
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.
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