Name
Abstract | ||
|---|---|---|
Molecular docking is a widely used computational technique that allows studying structure-based interactions complexes between biological objects at the molecular scale. The purpose of the current work is to develop a set of tools that allows performing inverse docking, i.e., to test at a large scale a chemical ligand on a large dataset of proteins, which has several applications on the field of drug research. We developed different strategies to parallelize/distribute the docking procedure, as a way to efficiently exploit the computational performance of multi-core and multi-machine (cluster) environments. The experiments conducted to compare these different strategies encourage the search for decomposing strategies as a way to improve the execution of inverse docking. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2488551.2488584 | EuroMPI |
Keywords | Field | DocType |
inverse docking method,different strategy,inverse docking,molecular scale,biological object,parallel strategy,computational performance,computational technique,molecular docking,large scale,large dataset,docking procedure | Computational Technique,Docking (molecular),Inverse,Lead Finder,Biological objects,Docking (dog),Protein–ligand docking,Artificial intelligence,Engineering,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 11 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Romain Vasseur | 1 | 1 | 0.69 |
| Stéphanie Baud | 2 | 1 | 1.71 |
| Luiz Angelo Steffenel | 3 | 62 | 13.84 |
| Xavier Vigouroux | 4 | 1 | 2.04 |
| Laurent Martiny | 5 | 1 | 0.69 |
| Michaël Krajecki | 6 | 96 | 16.01 |
| Manuel Dauchez | 7 | 6 | 9.68 |