Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space [data and software]

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Resource type: Software
Contributors: Creator: Degiacomi, Matteo ¹
Contact person: Degiacomi, Matteo ¹
Data collector: Degiacomi, Matteo ¹
Data curator: Degiacomi, Matteo ¹
Editor: Degiacomi, Matteo ¹

¹ Durham University, UK
Funder: Engineering and Physical Sciences Research Council
Research methods
Other description: This work features generative neural networks (autoencoders) trained on protein structures produced by molecular simulations. Autoencoders are used to obtain new, plausible conformations complementing and extending pre-existing ones, usable in a protein-protein docking scenario.
Keyword: molecular modelling
deep learning
molecular dynamics
proteins
Subject: Molecular dynamics
Proteins
Neural networks (Computer science)
Location
Language: English
Cited in
Identifier: ark:/32150/r26w924b81m
doi:10.15128/r26w924b81m
Rights: GNU General Public Licence 3 (GPL-3.0)
Publisher: Durham University
Date Created

Depositor: M.T. Degiacomi
Date Uploaded: 11 March 2019, 17:03:57
Date Modified: 23 March 2019, 11:03:58
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Characterization: File format: x-gzip (GZIP Format, GZIP)

Mime type: application/x-gzip

File size: 1236676114

Last modified: 2019:03:11 17:40:59+00:00

Filename: Degiacomi_Structure_2019.tar.gz

Original checksum: 21fa4248c866f632c9d5a49828e20cd1

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