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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WholeGraph: A Fast Graph Neural Network Training Framework with Multi-GPU Distributed Shared Memory Architecture
TL;DR: In this article , the authors present a fast training graph neural network framework, WholeGraph, based on a multi-GPU distributed shared memory architecture, which partitions the graph and corresponding node or edge features to multiple GPUs, eliminating the bottleneck of communication between CPU and GPUs during the training process.
Journal ArticleDOI
An interpretable machine learning approach to identify mechanism of action of antibiotics
TL;DR: InterPred as discussed by the authors is an interpretable technique for predicting bioactivity of small molecules and their mechanism of action, which has the same accuracy as the state of the art in bioactivity prediction, and enables assigning chemical moieties that are responsible for bioactivity.
Book ChapterDOI
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
Meng Liu,Shuiwang Ji +1 more
TL;DR: This work proposes the Neighbor2Seq to transform the hierarchical neighborhood of each node into a sequence that enables the subsequent mini-batch training for general deep learning operations, such as convolution and attention, that are designed for grid-like data and are shown to be powerful in various domains.
Journal ArticleDOI
Explainability and white box in drug discovery
TL;DR: In this paper , explainable artificial intelligence (XAI) techniques have been used to overcome the challenges in drug discovery, which can help further improve the drug discovery process and make the right decisions.
Proceedings Article
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease
Sayan Ghosal,Qiang Chen,Giulio Pergola,Aaron Goldman,William S Ulrich,Daniel R. Weinberger,Archana Venkataraman +6 more
TL;DR: A novel deep neural network for whole-genome imaging-genetics that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism (SNP) data is proposed, and it is shown that the biomarkers identified by the model are reproducible and closely associated with deflcits in schizophrenia.
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