A Deep Learning Approach to Antibiotic Discovery
Jonathan M. Stokes,Kevin Yang,Kyle Swanson,Wengong Jin,Andres Cubillos-Ruiz,Nina M. Donghia,Craig R. MacNair,Shawn French,Lindsey A. Carfrae,Zohar Bloom-Ackermann,Victoria M. Tran,Anush Chiappino-Pepe,Ahmed H. Badran,Ian W. Andrews,Ian W. Andrews,Ian W. Andrews,Emma J. Chory,George M. Church,Eric D. Brown,Tommi S. Jaakkola,Regina Barzilay,James J. Collins +21 more
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TLDR
A deep neural network capable of predicting molecules with antibacterial activity is trained and a molecule from the Drug Repurposing Hub-halicin- is discovered that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens.About:
This article is published in Cell.The article was published on 2020-02-20 and is currently open access. It has received 1002 citations till now.read more
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Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design.
TL;DR: This work trains a model on just 72 compounds to make predictions over a 10,833-compound library, identifying and experimentally validating compounds with nanomolar affinity for diverse kinases and whole-cell growth inhibition of Mycobacterium tuberculosis.
Journal ArticleDOI
Plant Natural Flavonoids Against Multidrug Resistant Pathogens.
Meirong Song,Ying Liu,Tingting Li,Xiaojia Liu,Zhihui Hao,Shuangyang Ding,Pharkphoom Panichayupakaranant,Kui Zhu,Jianzhong Shen +8 more
TL;DR: In this article, the potential of natural flavonoids from plants against multidrug resistant (MDR) bacteria, is demonstrated, and two compounds, α-mangostin (AMG) and isobavachalcone (IBC), are obtained.
Journal ArticleDOI
Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow
Chenglong Xie,Xuxu Zhuang,Zhangming Niu,Rui-Ting Ai,Sofie Lautrup,Shuangjia Zheng,Yinghui Jiang,Ruiyu Han,Tanima Sen Gupta,Shuqin Cao,Maria Jose Lagartos-Donate,Cui-Zan Cai,Liwei Xie,Domenica Caponio,Wen-Wen Wang,Tomas Schmauck-Medina,Jianying Zhang,He-ling Wang,Guofeng Lou,Xianglu Xiao,Wenhua Zhang,Konstantinos Palikaras,Guang Yang,Kim A. Caldwell,Guy A. Caldwell,Hanyuan Shen,Hilde Nilsen,Jiahua Lu,Evandro Fei Fang +28 more
TL;DR: In this paper , the authors report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds.
Journal ArticleDOI
Amelioration of Alzheimer’s disease pathology by mitophagy inducers identified via machine learning and a cross-species workflow
Chenglong Xie,Xuxu Zhuang,Zhangming Niu,Ruixue Ai,Sofie Lautrup,Shuangjia Zheng,Yinghui Jiang,Ruiyu Han,Tanima Sen Gupta,Shuqin Cao,Maria Jose Lagartos-Donate,Cui-Zan Cai,Liwei Xie,Domenica Caponio,Wen-Wen Wang,Tomas Schmauck-Medina,Jianying Zhang,He-ling Wang,Guofeng Lou,Xianglu Xiao,Wenhua Zhang,Konstantinos Palikaras,Guang Yang,Kim A. Caldwell,Guy A. Caldwell,Han-Ming Shen,Hilde Nilsen,Jia-Hong Lu,Evandro Fei Fang +28 more
TL;DR: In this article , the authors report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds.
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Machine Learning: New Ideas and Tools in Environmental Science and Engineering
Shifa Zhong,Kai Zhang,Majid Bagheri,Joel G. Burken,April Z. Gu,Baikun Li,Xingmao Ma,Babetta L. Marrone,Zhiyong Jason Ren,Joshua Schrier,Wei Shi,Haoyue Tan,Tianbao Wang,Xu Wang,Xu Wang,Bryan M. Wong,Xusheng Xiao,Xiong Yu,Junjie Zhu,Huichun Zhang +19 more
TL;DR: In this article, the authors explore the potential of ML to revolutionize data analysis and modeling in the field of environmental science and engineering (ESE) field, and cover the essential knowledge needed for such applications.
References
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One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products
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