A Framework for Inverse Virtual Screening Large-Scale Protein Targets Identification
Abstract
Molecular docking are widely used computational technics that allow studying structure-based interactions complexes between biological objects at the molecular scale. The purpose of the current work is to develop a framework that allows performing inverse virtual screening to test at a large scale a chemical ligand docking on a large dataset of proteins, which has several applications in the field of drug research. We developed different strategies to distribute the docking procedure, as a way to efficiently exploit the computational performance of multi-core and multi-machine (cluster) environments. This tool has been tested on 24 protein-ligand complexes taken from the Kellenberger dataset to show its ability to reproduce experimentally determined structures and binding affinities.
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