文献类型：期刊文献

英文题名：AI-guided few-shot inverse design of HDP-mimicking polymers against drug-resistant bacteria

作者：Wu, Tianyu[1];Zhou, Min[2];Zou, Jingcheng[3];Chen, Qi[3];Qian, Feng[1];Kurths, Juergen[4,5,6];Liu, Runhui[2,3];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Frontiers Sci Ctr Optogenet Tech Cell Met, Frontiers Sci Ctr Materiobiol & Dynam Chem, Key Lab Ultrafine Mat,Sch Mat Sci & Engn,Minist Ed, Shanghai 200237, Peoples R China;[4]Potsdam Inst Climate Impact Res PIK, D-14473 Potsdam, Germany;[5]Humboldt Univ, Inst Phys, D-10115 Berlin, Germany;[6]Fudan Univ, Res Inst Intelligent Complex Syst, Shanghai 200433, Peoples R China

年份：2024

卷号：15

期号：1

外文期刊名：NATURE COMMUNICATIONS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001330501700001)】；

基金：This research was supported by National Natural Science Foundation of China (Basic Science Center Program: No. 61988101), National Natural Science Foundation of China (No. T2325010, No. 22075078, No.62233005 and No. 62293502), National Key Research and Development Program of China (2022YFC2303100), German Research Foundation DFG (Project No.411803875), Fundamental Research Funds for the Central Universities (222202417006), Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission), and the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Shanghai AI Lab. We also thank the Research Center of Analysis and Test of East China University of Science and Technology for the help with the characterization. The authors also thank the support of the Analysis and Testing Center of School of Chemical Engineering, East China university of Science and Technology. Thanks for the staff members of the Integrated Laser Microscopy System at the National Facility for Protein Science in Shanghai (NFPS), Zhangjiang Lab, China, for providing technical support and assistance in data collection and analysis. Please refer to Journal-level guidance for any specific requirements.

语种：英文

摘要：Host defense peptide (HDP)-mimicking polymers are promising therapeutic alternatives to antibiotics and have large-scale untapped potential. Artificial intelligence (AI) exhibits promising performance on large-scale chemical-content design, however, existing AI methods face difficulties on scarcity data in each family of HDP-mimicking polymers (<102), much smaller than public polymer datasets (>105), and multi-constraints on properties and structures when exploring high-dimensional polymer space. Herein, we develop a universal AI-guided few-shot inverse design framework by designing multi-modal representations to enrich polymer information for predictions and creating a graph grammar distillation for chemical space restriction to improve the efficiency of multi-constrained polymer generation with reinforcement learning. Exampled with HDP-mimicking beta-amino acid polymers, we successfully simulate predictions of over 105 polymers and identify 83 optimal polymers. Furthermore, we synthesize an optimal polymer DM0.8iPen0.2 and find that this polymer exhibits broad-spectrum and potent antibacterial activity against multiple clinically isolated antibiotic-resistant pathogens, validating the effectiveness of AI-guided design strategy.