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Related Concept Videos

Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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Related Experiment Video

Updated: Jul 3, 2026

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis
11:29

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Grid computing in large pharmaceutical molecular modeling.

Brian L Claus1, Stephen R Johnson

  • 1Bristol-Myers Squibb Company, Princeton, NJ 08543-4000, USA. brian.claus@bms.com

Drug Discovery Today
|July 5, 2008
PubMed
Summary
This summary is machine-generated.

Pharmaceutical companies use grid computing to save money. Advances now allow complex molecular modeling on grids, enabling new scientific discoveries and flexible resource use.

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Area of Science:

  • Computational chemistry
  • Scientific computing

Background:

  • Major pharmaceutical companies utilize grid computing to reduce costs.
  • Limited suitability of existing applications for coarse-grained parallelization has restricted grid resource utilization in molecular modeling.

Purpose of the Study:

  • To explore how recent advances enable fine-grained parallel problems in grid environments.
  • To expand the scope of molecular modeling applications on grid infrastructure.

Main Methods:

  • Leveraging advances in grid infrastructure technology.
  • Implementing application research and redesign for fine-grained parallelization.

Main Results:

  • Fine-grained parallel problems, including quantum mechanics and molecular dynamics, are now accessible via grid computing.
  • Increased resource flexibility for load balancing and scheduling existing workloads.

Conclusions:

  • Recent technological and application advancements overcome previous limitations for grid-based molecular modeling.
  • Grid computing can now support a wider range of complex scientific problems, enhancing research capabilities and resource management.