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

Descriptions

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

File Details

Depositor
M.T. Degiacomi
Date Uploaded
Date Modified
23 March 2019, 11:03:58
Audit Status
Audits have not yet been run on this file.
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
Activity of users you follow
User Activity Date
User N. Syrotiuk has updated Coupling Molecular Dynamics and Deep Learning to Mine Protein Conformational Space [data and software] over 5 years ago