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本文引用的文献

1
Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2.深度学习驱动的药物发现:应对严重急性呼吸综合征冠状病毒2
Front Microbiol. 2021 Oct 28;12:739684. doi: 10.3389/fmicb.2021.739684. eCollection 2021.
2
Towards the sustainable discovery and development of new antibiotics.迈向新型抗生素的可持续发现与开发。
Nat Rev Chem. 2021;5(10):726-749. doi: 10.1038/s41570-021-00313-1. Epub 2021 Aug 19.
3
National Estimates of Healthcare Costs Associated With Multidrug-Resistant Bacterial Infections Among Hospitalized Patients in the United States.美国住院患者中多重耐药菌感染相关医疗费用的国家估计数。
Clin Infect Dis. 2021 Jan 29;72(Suppl 1):S17-S26. doi: 10.1093/cid/ciaa1581.
4
Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae.发现β-内酰胺酶 CMY-10 抑制剂用于联合治疗多药耐药肠杆菌科。
PLoS One. 2021 Jan 15;16(1):e0244967. doi: 10.1371/journal.pone.0244967. eCollection 2021.
5
Simultaneous elucidation of antibiotic mechanism of action and potency with high-throughput Fourier-transform infrared (FTIR) spectroscopy and machine learning.利用高通量傅里叶变换红外(FTIR)光谱和机器学习同时阐明抗生素作用机制和效价。
Appl Microbiol Biotechnol. 2021 Feb;105(3):1269-1286. doi: 10.1007/s00253-021-11102-7. Epub 2021 Jan 14.
6
Molecular diversification of antimicrobial peptides from the wolf spider Lycosa sinensis venom based on peptidomic, transcriptomic, and bioinformatic analyses.基于肽组学、转录组学和生物信息学分析的狼蛛 Lycosa sinensis 毒液中抗菌肽的分子多样化。
Acta Biochim Biophys Sin (Shanghai). 2020 Dec 11;52(11):1274-1280. doi: 10.1093/abbs/gmaa107.
7
Immuno-Informatics Based Peptides: An Approach for Vaccine Development Against Outer Membrane Proteins of Pseudomonas Genus.基于免疫信息学的肽:针对假单胞菌属外膜蛋白的疫苗开发方法。
IEEE/ACM Trans Comput Biol Bioinform. 2022 Mar-Apr;19(2):966-973. doi: 10.1109/TCBB.2020.3032651. Epub 2022 Apr 1.
8
Correlation between hemolytic activity, cytotoxicity and systemic in vivo toxicity of synthetic antimicrobial peptides.合成抗菌肽的溶血活性、细胞毒性与体内全身毒性之间的相关性。
Sci Rep. 2020 Aug 6;10(1):13206. doi: 10.1038/s41598-020-69995-9.
9
Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli.人工神经网络在预测具有抗大肠杆菌抗菌活性的新物质中的应用。
J Appl Microbiol. 2021 Jan;130(1):40-49. doi: 10.1111/jam.14763. Epub 2020 Jul 16.
10
Predicting Phenotypic Polymyxin Resistance in Klebsiella pneumoniae through Machine Learning Analysis of Genomic Data.通过对基因组数据进行机器学习分析预测肺炎克雷伯菌的表型多粘菌素耐药性
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人工智能与抗生素发现

Artificial Intelligence and Antibiotic Discovery.

作者信息

David Liliana, Brata Anca Monica, Mogosan Cristina, Pop Cristina, Czako Zoltan, Muresan Lucian, Ismaiel Abdulrahman, Dumitrascu Dinu Iuliu, Leucuta Daniel Corneliu, Stanculete Mihaela Fadygas, Iaru Irina, Popa Stefan Lucian

机构信息

2nd Medical Department, "Iuliu Hatieganu" University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania.

Faculty of Environmental Protection, University of Oradea, 410048 Oradea, Romania.

出版信息

Antibiotics (Basel). 2021 Nov 10;10(11):1376. doi: 10.3390/antibiotics10111376.

DOI:10.3390/antibiotics10111376
PMID:34827314
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8614913/
Abstract

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.

摘要

近几十年来,由于该领域研究减少、药物研发公司投资回报率低、研究过程漫长且艰难、候选分子成功率低、农场抗生素使用增加以及抗生素整体使用不当,一场新的抗生素危机正在显现。这导致一系列病原体产生抗生素耐药性,对公共卫生系统构成严重威胁,同时也推高了住院和治疗成本。此外,如果学术机构和卫生机构之间没有采取适当行动和开展合作,可能会出现灾难性趋势,有可能回到抗生素出现之前的时代。然而,新兴的基于人工智能的技术已开始进入抗生素和药物研发领域,为这个日益严重的问题提供了新视角。通过识别活性化合物的算法可以实现更廉价、更快速的研究,从而进一步加速新型抗生素的研发,这是解决当前抗生素危机的关键一步。本综述的目的是全面概述当前用于抗生素发现的基于人工智能的技术,以及它们对工业部门的技术和经济影响。