Over the last two decades, an increasing number of chemists have turned to the computer to predict the results of experiments beforehand or to help interpret the results of experiments. Skepticism on the part of laboratory chemists has gradually evaporated as the computational results have made contact with, and even anticipated, experimental findings. When the 1998 Nobel Prize in Chemistry was awarded recently to two scientists, Walter Kohn and John Pople, who originated some of the first successful methods in computational chemistry, the award was seen as an affirmation of the value of computational chemistry to the field of chemistry.

"We've come a long way," said Peter Kollman of the Department of Pharmaceutical Chemistry at UC San Francisco (UCSF). "But while we've come a long way, we can see that we've still got a long way to go."

Now, as part of an NPACI Strategic Application Collaboration, AMBER's performance is being improved by 50 percent to 65 percent.

AMBER stands for Assisted Model Building with Energy Refinement. The code's successes include its use to study protein folding, to study the relative free energies of binding of two ligands to a given host (or two hosts to a given ligand), to investigate the sequence-dependent stability of proteins and nucleic acids, and to find the relative solvation free energies of different molecules in various liquids. Hundreds of contributions to the scientific literature reflect the use of AMBER.

(This news summarized from the San Diego Super Computing Center and original full text can be reached their web site)

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