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Pharmaceutical

GridSystems has a very extensive experience on the implementation of Grid technology in the pharmaceutical, bioinformatics, and chemical sectors. This experience has been acquired working very close with our customers in research centers, universities, and pharmaceutical companies. GridSystems solutions allow the optimization of all the IT infrastructure of companies and research centers to produce a drastic reduction of the computation time and costs of simulation processes.

Some relevant examples of our experience are the Docking analysis in Drug Design, DNA & protein sequence analysis in Genetics research, comparative genome analysis, or the design of chemical compounds. Applying grid solutions, pharmaceutical companies have seen their Drug Design processes reduced in time and costs; research laboratories have optimized the use of their computational resources, and they have been able to extend and scale their research processes.

Pharmaceutical companies’ design of drugs can greatly benefit from Fura EE in two of their core processes. One is the design of drugs with the chemical simulations that they require. The design process based on a Gaussian package has been proven to easily implement in an Fura EE platform. Docking has also been proven to work extremely well.

For pharmaceutical companies, the business value of Fura EE is the scalability and the speed enhancement of their processes. Computations that traditionally may take weeks are reduced to days or hours, and others that would simply be out of reach, become feasible with the same IT resources. Besides, the possibility to increase the base of the studies makes research more effective. The features of Fura EE allow pharmaceutical companies shorten the design cycle, providing companies with a competitive vantage in the market.

  Example: Taisho    
 

The installation of our Grid products at Taisho Pharmaceutical allowed the research groups to run in-silico docking experiments, covering a broad range of compounds and proteins (peptides and drugs) in a much more efficient way. These analyses require a great amount of computing time to be carried out. Using conventional computational methods, they were considered unfeasible.

 
Now, the computing time is reduced drastically. The time-to-market for new drugs has been reduced, and the cost for the first stage (drug discovery) is substantially lower.