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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8307493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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