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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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A comprehensive study on the prediction reliability of graph neural networks for virtual screening.

TL;DR: This work aims to propose guidelines for training reliable models, and investigates the effects of model architectures, regularization methods, and loss functions on the prediction performance and reliability of classification results, and evaluates prediction reliability of models on virtual screening scenario.
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AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials

TL;DR: AB-DB as mentioned in this paper is an open database of all-atom force-field parameters, molecular dynamics trajectories, quantum-mechanical properties, and curated physico-chemical descriptors of antimicrobial compounds.
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A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

TL;DR: A flexible machine-learning framework that utilizes diverse data types to effectively search through the large design space of both sequential and simultaneous combination therapies across hundreds of simulated growth conditions and pathogen metabolic states can serve as a useful guide for the selection of robustly synergistic drug combinations.
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Drug-likeness scoring based on unsupervised learning

TL;DR: In this article , a language model based on a recurrent neural network for unsupervised learning was proposed for drug-likeness prediction, which showed relatively consistent performance across different datasets, unlike such classification models.
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Characterization of hydration and dry shrinkage behavior of cement emulsified asphalt composites using deep learning

TL;DR: In this article, a study of hydration and dry shrinkage behavior of cement emulsified asphalt composites (CEACs) through a deep-learning framework is presented, which consists of two parts: generative adversarial networks (GANs) and deep neural networks (DNNs).
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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