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AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on H...

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Autores principales: Darme, Pierre, Dauchez, Manuel, Renard, Arnaud, Voutquenne-Nazabadioko, Laurence, Aubert, Dominique, Escotte-Binet, Sandie, Renault, Jean-Hugues, Villena, Isabelle, Steffenel, Luiz-Angelo, Baud, Stéphanie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307493/
https://www.ncbi.nlm.nih.gov/pubmed/34299110
http://dx.doi.org/10.3390/ijms22147489
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author Darme, Pierre
Dauchez, Manuel
Renard, Arnaud
Voutquenne-Nazabadioko, Laurence
Aubert, Dominique
Escotte-Binet, Sandie
Renault, Jean-Hugues
Villena, Isabelle
Steffenel, Luiz-Angelo
Baud, Stéphanie
author_facet Darme, Pierre
Dauchez, Manuel
Renard, Arnaud
Voutquenne-Nazabadioko, Laurence
Aubert, Dominique
Escotte-Binet, Sandie
Renault, Jean-Hugues
Villena, Isabelle
Steffenel, Luiz-Angelo
Baud, Stéphanie
author_sort Darme, Pierre
collection PubMed
description Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.
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spelling pubmed-83074932021-07-25 AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability Darme, Pierre Dauchez, Manuel Renard, Arnaud Voutquenne-Nazabadioko, Laurence Aubert, Dominique Escotte-Binet, Sandie Renault, Jean-Hugues Villena, Isabelle Steffenel, Luiz-Angelo Baud, Stéphanie Int J Mol Sci Article Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets. MDPI 2021-07-13 /pmc/articles/PMC8307493/ /pubmed/34299110 http://dx.doi.org/10.3390/ijms22147489 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Darme, Pierre
Dauchez, Manuel
Renard, Arnaud
Voutquenne-Nazabadioko, Laurence
Aubert, Dominique
Escotte-Binet, Sandie
Renault, Jean-Hugues
Villena, Isabelle
Steffenel, Luiz-Angelo
Baud, Stéphanie
AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title_full AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title_fullStr AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title_full_unstemmed AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title_short AMIDE v2: High-Throughput Screening Based on AutoDock-GPU and Improved Workflow Leading to Better Performance and Reliability
title_sort amide v2: high-throughput screening based on autodock-gpu and improved workflow leading to better performance and reliability
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8307493/
https://www.ncbi.nlm.nih.gov/pubmed/34299110
http://dx.doi.org/10.3390/ijms22147489
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