Computational Chemistry
for Drug Discovery and Materials Research

White Paper   



LAMMPS on Advanced
SGI Architectures
(PDF 2.3M)
Computational chemistry helps predict both the nature and function of new chemical compounds and materials. It has enhanced the quality of life by improving products from a variety of industries such as pharmaceuticals, plastics, microprocessors, glass, metal, paint, aerospace and automotive.

In drug discovery, effective use of computational chemistry shortens the development cycle for new drugs and provides pharmaceutical companies a competitive advantage with faster time-to-discovery and reduced costs. In materials research, initially a trial-by-error process, advances in laboratory chemistry techniques have helped the field evolve into a rigorous experimental science.

Today, significant advancements in software development, the ability to perform complex data analysis, and the ability to predict the properties of new chemical compounds and materials in silicon before any laboratory effort is expended have brought enormous efficiency to the materials research and drug discovery communities.

In today's global marketplace, with inexpensive personnel resources, efficient distributed manufacturing and distribution, and aggressive margin-depleting pricing, development groups are required to produce differentiated products more efficiently and more effectively. SGI solutions for computational chemistry such as SGI® UV shared memory systems, SGI® ICE and Rackable® cluster systems, and SGI® InfiniteStorage data management options provide researchers the tools to expand their analysis options and improve the performance of the analyses by enabling:

  • Exploration of all modeling possibilities on a single server
  • Larger, more complex modeling and analysis performed in record time
  • Modeling at higher theory levels than possible on any other system
  • Access to broader research opportunities and more thorough path to insight
  • A robust portfolio of key applications ported and tuned for computational chemistry

Additional Information