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Analysis of Organochlorine Pesticides in a Soil Sample by a Modified QuEChERS Approach Using Ammonium Formate
Published on: January 20, 2023
Open access databases available for the pesticide lead discovery
Daozhong Wang1, Hua Deng2, Tao Zhang3
1State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China; Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan 430070, China; College of Veterinary Medicine, National Reference Laboratory of Veterinary Drug Residues (HZAU) and MAO Key Laboratory for Detection of Veterinary Drug Residues, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health,Huazhong Agricultural University, Shenzhen 518000, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China.
Abstract:
Pesticide research is a multi-disciplinary collaborative study, and big data analysis based on integrating information from databases benefits decision-making in pesticide research. In the last 40 years, dozens of pesticide-related databases have been built up to describe their biological activities, toxicity, modes of action, and environmental risks, etc. However, these data are scattered and overlapping in different databases in multiple inconsistent formats, which is not convenient for information analysis and comparison. In this study, the content of 26 open access databases related to pesticide research was illustrated according to the information provided for the ligand-based drug design (LBDD) and receptor-based (or structure-based drug design, SBDD), and was summarized into three categories:1) the correspondence between the chemical structures and functional properties (biological activity, resistance, toxicity, environmental adaptation); 2) action mode study (target identification, target structures, and biological pathways); 3) computational servers for pesticide design. To our knowledge, this is the first review about the open access databases for pesticide research. The data classification could facilitate the information accessibility for pesticide research, and speed up the decision-making process in pesticide discovery.
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