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The cloud and other new computational methods to improve molecular modelling.

Oliver Korb1, Paul W Finn, Gareth Jones

  • 1Cambridge Crystallographic Data Centre , 12 Union Road, Cambridge, CB2 1EZ , UK +44 1223 336408 ; +44 1223 336033 ; korb@ccdc.cam.ac.uk.

Expert Opinion on Drug Discovery
|August 23, 2014
PubMed
Summary

Leveraging grid, cloud, and graphics processing units (GPUs) can significantly accelerate demanding computational modeling techniques in drug discovery. Careful selection of these computing architectures is key to improving molecular modeling efficiency.

Keywords:
cloud computingdockinggraphics processing unitsgrid computingmolecular dynamicsmolecular modellingmulti-core architecturesvirtual screening

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Computational modeling is integral to industrial and academic drug discovery.
  • Techniques like virtual screening and molecular dynamics are computationally intensive.
  • Parallel computing architectures offer solutions to accelerate these demanding tasks.

Purpose of the Study:

  • To review recent advancements in grid, cloud, and GPU computing for molecular modeling.
  • To provide an overview of molecular modeling applications on these platforms.
  • To discuss theoretical and practical considerations of these hardware architectures.

Main Methods:

  • Review of grid computing developments.
  • Analysis of cloud computing advancements.
  • Exploration of graphics processing unit (GPU) computing applications in molecular modeling.

Main Results:

  • Grid, cloud, and GPU computing can substantially reduce execution times for molecular modeling tasks.
  • These technologies enable the analysis of larger datasets and the use of more accurate methods.
  • Specific challenges and limitations exist for each computational platform.

Conclusions:

  • Appropriate selection of computing technologies can considerably enhance molecular modeling processes.
  • Future work must address existing issues within individual computational platforms.
  • Current research prioritizes optimizing existing algorithms over developing novel methods tailored to new hardware.