A Deep Learning Approach to Antibiotic Discovery.
A Deep Learning Approach to Antibiotic Discovery.
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DOI：
10.1016/j.cell.2020.01.021
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发表时间：
2020-02-20
期刊：
影响因子：
64.5
通讯作者：
Collins JJ
中科院分区：
文献类型：
--
作者：
Stokes JM;Yang K;Swanson K;Jin W;Cubillos-Ruiz A;Donghia NM;MacNair CR;French S;Carfrae LA;Bloom-Ackermann Z;Tran VM;Chiappino-Pepe A;Badran AH;Andrews IW;Chory EJ;Church GM;Brown ED;Jaakkola TS;Barzilay R;Collins JJ
由于耐抗生素细菌的迅速出现，越来越需要发现新的抗生素。为了应对这一挑战，我们训练了一个能够预测具有抗菌活性分子的深度神经网络。我们对多个化学文库进行了预测，并从药物再利用中心发现了一种分子——halicin——它在结构上与传统抗生素不同，并对广泛的系统发育谱系的病原体显示出杀菌活性，包括结核分枝杆菌和耐碳青霉烯的肠杆菌科。在小鼠模型中，盐酸盐也能有效治疗艰难梭菌和泛耐药鲍曼不动杆菌感染。此外，从ZINC15数据库中筛选的1.07亿分子中，我们的模型从23个离散的经验测试预测中确定了8种与已知抗生素结构遥远的抗菌化合物。这项工作强调了深度学习方法的实用性，通过发现结构独特的抗菌分子来扩大我们的抗生素库。
Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub – halicin – that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens, including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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影响因子：
5.3
作者：
Farha, Maya A.;French, Shawn;Brown, Eric D.
通讯作者：
Brown, Eric D.
影响因子：
--
作者：
Farha, Maya A.;Verschoor, Chris P.;Brown, Eric D.
通讯作者：
Brown, Eric D.
影响因子：
4.9
作者：
ANGUS, BL;CAREY, AM;HANCOCK, REW
通讯作者：
HANCOCK, REW
影响因子：
14.9
作者：
Keseler IM;Mackie A;Peralta-Gil M;Santos-Zavaleta A;Gama-Castro S;Bonavides-Martínez C;Fulcher C;Huerta AM;Kothari A;Krummenacker M;Latendresse M;Muñiz-Rascado L;Ong Q;Paley S;Schröder I;Shearer AG;Subhraveti P;Travers M;Weerasinghe D;Weiss V;Collado-Vides J;Gunsalus RP;Paulsen I;Karp PD
通讯作者：
Karp PD
DOI：
10.1073/pnas.120163297
发表时间：
2000-06-06
影响因子：
11.1
作者：
Datsenko, KA;Wanner, BL
通讯作者：
Wanner, BL