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A Deep Learning Approach to Antibiotic Discovery

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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.
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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.

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Proceedings Article

Decoupling the Depth and Scope of Graph Neural Networks

TL;DR: This work proposes a design principle to decouple the depth and scope of GNNs – to generate representation of a target entity, first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
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How to Enter a Bacterium: Bacterial Porins and the Permeation of Antibiotics.

TL;DR: In this article, the authors discuss the recent progress, both experimentally as well as computationally, in understanding the structure function relationship of outer-membrane channels of Gram-negative pathogens, mainly focusing on the transport of antibiotics.
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Identification of antimicrobial peptides from the human gut microbiome using deep learning

TL;DR: This study combined multiple natural language processing neural network models, including LSTM, Attention and BERT, to form a unified pipeline for candidate AMP identification from human gut microbiome data and demonstrated significant efficacy in lowering bacterial load against a mouse model of bacterial lung infection.
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Deep learning identifies synergistic drug combinations for treating COVID-19.

TL;DR: In this article, a neural network architecture was proposed to jointly learn drug-target interaction and drug-drug synergy for new diseases with limited combination data, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Designing and understanding light-harvesting devices with machine learning

TL;DR: This work presents opportunities to gain detailed scientific insights into the underlying principles governing light-harvesting phenomena and can accelerate the fabrication of light-Harvesting devices.
References
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Journal ArticleDOI

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
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Fast and accurate short read alignment with Burrows–Wheeler transform

TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
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One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products

TL;DR: A simple and highly efficient method to disrupt chromosomal genes in Escherichia coli in which PCR primers provide the homology to the targeted gene(s), which should be widely useful, especially in genome analysis of E. coli and other bacteria.
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Extended-Connectivity Fingerprints

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.
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