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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Message-Passing Neural Network Based Multi-Task Deep-Learning Framework for COSMO-SAC based σ-profile and VCOSMO Prediction
Jun Zhang,Qin Wang,Weifeng Shen +2 more
TL;DR: In this article , a message-passing neural network based feedforward neural network (MPNN-FNN) is proposed for rapid and accurate predicting COSMO-SAC based σ-profile and cavity volumes.
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Cocrystal prediction by artificial neural networks
TL;DR: In this paper, a data-driven cocrystal prediction method based on two types of artificial neural network models and data from the Cambridge Structural Database (CSDB) is presented.
Journal ArticleDOI
A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.
TL;DR: Based on machine learning methods and data mining techniques, this article reviewed the antimicrobial resistance data storage and analysis techniques, antimicrobial assessment methods and associated risk assessment methods for antimicrobial resistant, and antimicrobial prediction methods.
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The pursuit of mechanism of action: uncovering drug complexity in TB drug discovery.
TL;DR: In this paper, the authors describe a plethora of efforts based upon genetic, metabolomic, biochemical, and computational approaches to investigate TB drug MOAs and assess these different platforms for their strengths and limitations in tuberculosis drug MOA elucidation in the context of Mtb pathogenesis.
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Rapid antimicrobial susceptibility testing by stimulated Raman scattering metabolic imaging and morphological deformation of bacteria.
TL;DR: A new method based on stimulated Raman scattering (SRS) microscopy is reported, which measures both the metabolic activity and the morphological deformation of bacteria to determine the antimicrobial susceptibility of β-lactam antibiotics rapidly.
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One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products
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Extended-Connectivity Fingerprints
David Rogers,Mathew Hahn +1 more
TL;DR: A description of their implementation has not previously been presented in the literature, and ECFPs can be very rapidly calculated and can represent an essentially infinite number of different molecular features.