Crops will be harmed by fungi,pests and weeds during their growth.In the past half century,scientists have conducted in-depth research on natural products extracted from microorganisms,plants or animals,and used natur...Crops will be harmed by fungi,pests and weeds during their growth.In the past half century,scientists have conducted in-depth research on natural products extracted from microorganisms,plants or animals,and used natural products and their derivatives as the lead for pesticides discovery.Natural product pesticides have the advantages of easy degradation in the environment,selective control and safety to non-target organisms.This review summarizes the studies on natural products pesticides in recent years,including natural products of chemical modification and biosynthesis,mainly fungicides,herbicides,insecticides and acaricides.We classify natural products according to their active fragments,and discuss their effects on the control of agricultural fungi,pests and weeds.Ultimately,we found that lead discovery based on natural products has great advantages in pesticide development.展开更多
The latest review published in Nature Reviews Drug Discovery by Michael W.Mullowney and co-authors focuses on the use of artificial intelligence techniques,specifically machine learning,in natural product drug discove...The latest review published in Nature Reviews Drug Discovery by Michael W.Mullowney and co-authors focuses on the use of artificial intelligence techniques,specifically machine learning,in natural product drug discovery.The authors discussed various applications of AI in this field,such as genome and metabolome mining,structural characterization of natural products,and predicting targets and biological activities of these compounds.They also highlighted the challenges associated with creating and managing large datasets for training algorithms,as well as strategies to address these obstacles.Additionally,the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.展开更多
基金the financial support for this research from the National Natural Science Foundation of China(22177051,32061143045)the National Key Research and Development Program of China(2021YFD1700103)+1 种基金Sichuan Key Research and Development Program(22ZDYF0186,2021YFN0134)the College Student Research Training Program from Nanjing Agricultural University(202110307002T).
文摘Crops will be harmed by fungi,pests and weeds during their growth.In the past half century,scientists have conducted in-depth research on natural products extracted from microorganisms,plants or animals,and used natural products and their derivatives as the lead for pesticides discovery.Natural product pesticides have the advantages of easy degradation in the environment,selective control and safety to non-target organisms.This review summarizes the studies on natural products pesticides in recent years,including natural products of chemical modification and biosynthesis,mainly fungicides,herbicides,insecticides and acaricides.We classify natural products according to their active fragments,and discuss their effects on the control of agricultural fungi,pests and weeds.Ultimately,we found that lead discovery based on natural products has great advantages in pesticide development.
基金supported in part by the National Key Research and Development Program of China(2021YFD1700100,2023YFD1700500)the National Natural Science Foundation of China(22177051)+1 种基金the Fundamental Research Funds for the Central Universities(KYCYXT2022010)Sichuan Key Research and Development Program(22ZDYF0186,2021YFN0134).
文摘The latest review published in Nature Reviews Drug Discovery by Michael W.Mullowney and co-authors focuses on the use of artificial intelligence techniques,specifically machine learning,in natural product drug discovery.The authors discussed various applications of AI in this field,such as genome and metabolome mining,structural characterization of natural products,and predicting targets and biological activities of these compounds.They also highlighted the challenges associated with creating and managing large datasets for training algorithms,as well as strategies to address these obstacles.Additionally,the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.
