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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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Book ChapterDOI

The rise of artificial intelligence in healthcare applications

TL;DR: In this chapter, some of the major applications of AI in healthcare will be discussed covering both the applications that are directly associated with healthcare and those in the healthcare value chain such as drug development and ambient assisted living.
Journal ArticleDOI

Structure-Based Virtual Screening: From Classical to Artificial Intelligence.

TL;DR: An overview of the challenges involved in the use of CADD to performSBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process are presented.
Posted Content

Debiased Contrastive Learning

TL;DR: A debiased contrastive objective is developed that corrects for the sampling of same-label datapoints, even without knowledge of the true labels, and consistently outperforms the state-of-the-art for representation learning in vision, language, and reinforcement learning benchmarks.
Journal ArticleDOI

How Machine Learning Will Transform Biomedicine

TL;DR: A vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health monitoring, where the goal is to maintain health through a range of diseases and the normal aging process is outlined.
Journal ArticleDOI

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

TL;DR: In this article, Artificial Neural Networks and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity.
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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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.
Journal ArticleDOI

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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