文献类型：期刊文献

英文题名：Discovery of Potential Neonicotinoid Insecticides by an Artificial Intelligence Generative Model and Structure-Based Virtual Screening

作者：Kong, Yijin[1];Zhou, Cong[1];Tan, Du[1];Xu, Xiaoyong[1];Li, Zhong[1];Cheng, Jiagao[1]

机构：[1]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab Chem Biol, Shanghai 200237, Peoples R China

年份：2024

卷号：72

期号：10

起止页码：5145

外文期刊名：JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY

收录：;EI(收录号：20241015676422);WOS:【SCI-EXPANDED(收录号:WOS:001178595300001)】；

基金：The authors gratefully acknowledge the National Key Research and Development Program of China (2023YFD1700501), National Natural Science Foundation of China (21977030), and Natural Science Foundation of Shanghai (22ZR1415600) for the financial support.

语种：英文

外文关键词：neonicotinoid insecticides; generative model; transfer learning; virtual screening; moleculardynamics simulations

摘要：The identification of neonicotinoid insecticides bearing novel scaffolds is of great importance for pesticide discovery. Here, artificial intelligence-based tools and virtual screening strategy were integrated to discover potential leads of neonicotinoid insecticides. A deep generative model was successfully constructed using a recurrent neural network combined with transfer learning. The model evaluation showed that the pretrained model could accurately grasp the SMILES grammar of drug-like molecules and generate potential neonicotinoid compounds after transfer learning. The generated molecules were evaluated by hierarchical virtual screening, hits were subjected to a similarity search, and the most similar structures were purchased for the bioassay. Compounds A2 and A5 displayed 52.5 and 50.3% mortality rates against Aphis craccivora at 100 mg/L, respectively. The docking study indicated that these two compounds have similar binding modes to neonicotinoids, which were verified by further molecular dynamics simulations.