Spatiotemporal identification of druggable binding sites using deep learning

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2399-3642

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Spatiotemporal identification of druggable binding sites using deep learning ; volume:3 ; number:1 ; day:27 ; month:10 ; year:2020 ; pages:1-12 ; date:12.2020
Communications biology ; 3, Heft 1 (27.10.2020), 1-12, 12.2020

Creator: Kozlovskii, Igor
Popov, Petr

Contributor: SpringerLink (Online service)

DOI: 10.1038/s42003-020-01350-0

URN: urn:nbn:de:101:1-2020120619273070710619

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:26 AM CEST

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Kozlovskii, Igor
Popov, Petr
SpringerLink (Online service)

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Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

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Spatiotemporal identification of druggable binding sites using deep learning

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Other Objects (12)

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable Allosteric Sites in β‐Propeller Lectins

DPI_CDF: druggable protein identifier using cascade deep forest

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

Deep learning for de-convolution of Smad2 versus Smad3 binding sites

Identification of a druggable binding pocket in the spike protein reveals a key site for existing drugs potentially capable of combating Covid-19 infectivity

Prediction and Validation of a Druggable Site on Virulence Factor of Drug Resistant Burkholderia cenocepacia **

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Cysteine metabolic circuitries: druggable targets in cancer

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

PUResNet: prediction of protein-ligand binding sites using deep residual neural network

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable Allosteric Sites in β‐Propeller Lectins

DPI_CDF: druggable protein identifier using cascade deep forest

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

Deep learning for de-convolution of Smad2 versus Smad3 binding sites

Identification of a druggable binding pocket in the spike protein reveals a key site for existing drugs potentially capable of combating Covid-19 infectivity

Prediction and Validation of a Druggable Site on Virulence Factor of Drug Resistant Burkholderia cenocepacia **

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Cysteine metabolic circuitries: druggable targets in cancer

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

PUResNet: prediction of protein-ligand binding sites using deep residual neural network

Comprehensive detection and characterization of human druggable pockets through binding site descriptors

Druggable Allosteric Sites in β‐Propeller Lectins

DPI_CDF: druggable protein identifier using cascade deep forest

Highly accurate carbohydrate-binding site prediction with DeepGlycanSite

RBPsuite: RNA-protein binding sites prediction suite based on deep learning

Deep learning for de-convolution of Smad2 versus Smad3 binding sites

Identification of a druggable binding pocket in the spike protein reveals a key site for existing drugs potentially capable of combating Covid-19 infectivity

Prediction and Validation of a Druggable Site on Virulence Factor of Drug Resistant Burkholderia cenocepacia **

Predicting Ca2+ and Mg2+ ligand binding sites by deep neural network algorithm

Cysteine metabolic circuitries: druggable targets in cancer

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

PUResNet: prediction of protein-ligand binding sites using deep residual neural network

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