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AuPosSOM specifications

Information


Unique identifier OMICS_17029
Name AuPosSOM
Alternative name Automatic analysis of poses using self-organizing maps
Interface Web user interface
Restrictions to use None
Computer skills Basic
Version 2.1
Stability Stable
Maintained Yes

Maintainer


  • person_outline Gildas Bertho

Additional information


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Information


Unique identifier OMICS_17029
Name AuPosSOM
Alternative name Automatic analysis of poses using self-organizing maps
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Windows
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

Versioning


No version available

Maintainer


  • person_outline Gildas Bertho

Additional information


A registration is needed to access to tools.

Publication for Automatic analysis of poses using self-organizing maps

AuPosSOM citations

 (4)
library_books

In silico based identification of human α enolase inhibitors to block cancer cell growth metabolically

2017
Drug Des Devel Ther
PMCID: 5695255
PMID: 29180852
DOI: 10.2147/DDDT.S149214

[…] used to generate its ranking. Chemical structures with a good docking score were further analyzed and classified according to their contact modes and strengths with amino acids of α-enolase using the AuPosSOM 2.1 web interface (https://www.biomedicale.univ-paris5.fr/aupossom/)., The clustering began with an initial raw training phase, in which α began at 0.2 and ended at 0, radius began at 6 Å and […]

library_books

Binding mode of triazole derivatives as aromatase inhibitors based on docking, protein ligand interaction fingerprinting, and molecular dynamics simulation studies

2017
PMCID: 5333476
PMID: 28255310
DOI: 10.4103/1735-5362.199043

[…] into a drug likeness filter using DruLito software (V. 1.0). Subsequently, molecular docking simulations and protein ligand interaction fingerprints analysis were applied using Autodock4 (V. 4.2) and AuPosSOM (V. 2.1) softwares, respectively. Due to great role of molecular dynamic (MD) simulations in drug design(), the best compound was selected for further MD experiment. It should be mentioned th […]

library_books

Footprinting of Inhibitor Interactions of In Silico Identified Inhibitors of Trypanothione Reductase of Leishmania Parasite

2012
Sci World J
PMCID: 3322522
PMID: 22550471
DOI: 10.1100/2012/963658

[…] diverse compound sets aiding in classification of differential binding modes exhibited by small molecules at the active site of TR. The interactions were clustered from protein-ligand complexes using AuPosSOM [], and they were also classified into subgroups. Four different major clusters were obtained based upon the interaction of inhibitors on the active site of TR; each cluster exhibiting differ […]

library_books

Structure Based Virtual Screening for Drug Discovery: a Problem Centric Review

2012
PMCID: 3282008
PMID: 22281989
DOI: 10.1208/s12248-012-9322-0

[…] heir target to display activity and also to tackle the inefficiency of traditional clustering of docking poses, Bouvier et al. have proposed the Automatic analysis of Poses using Self-Organizing Map (AuPosSOM) method for pose ranking with careful analysis of interatomic contacts between the docked ligand and the target (). They have demonstrated that it is possible to differentiate active compound […]

Citations

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AuPosSOM institution(s)
Université Paris Descartes, Sorbonne, Paris, France; Institut Pasteur, Paris, France

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