Literature DB >> 18338845

Hydra: a self regenerating high performance computing grid for drug discovery.

Drew Bullard1, Alberto Gobbi, Matthew A Lardy, Charles Perkins, Zach Little.   

Abstract

Computer aided drug design is progressing and playing an increasingly important role in drug discovery. Computational methods are being used to evaluate larger and larger numbers of real and virtual compounds. New methods based on molecular simulations that take protein and ligand flexibility into account also contribute to an ever increasing need for compute time. Computational grids are therefore becoming a critically important tool for modern drug discovery, but can be expensive to deploy and maintain. Here, we describe the low cost implementation of a 165 node, computational grid at Anadys Pharmaceuticals. The grid makes use of the excess computing capacity of desktop computers deployed throughout the company and of outdated desktop computers which populate a central computing grid. The performance of the grid grows automatically with the size of the company and with advances in computer technology. To ensure the uniformity of the nodes in the grid, all computers are running the Linux operating system. The desktop computers run Linux inside MS Windows using coLinux as virtualization software. HYDRA has been used to optimize computational models, for virtual screening and for lead optimization.

Mesh:

Year:  2008        PMID: 18338845     DOI: 10.1021/ci700396b

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  2 in total

1.  Software platform virtualization in chemistry research and university teaching.

Authors:  Tobias Kind; Tim Leamy; Julie A Leary; Oliver Fiehn
Journal:  J Cheminform       Date:  2009-11-16       Impact factor: 5.514

2.  Search for β2 adrenergic receptor ligands by virtual screening via grid computing and investigation of binding modes by docking and molecular dynamics simulations.

Authors:  Qifeng Bai; Yonghua Shao; Dabo Pan; Yang Zhang; Huanxiang Liu; Xiaojun Yao
Journal:  PLoS One       Date:  2014-09-17       Impact factor: 3.240

  2 in total

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