The ongoing pressure to find the next blockbuster drug is felt nowhere more keenly than in the area of drug discovery, in which ever-increasing volumes of data need to be thoroughly analysed and likely candidates must be rapidly identified and patented. IT is already essential to the generation and processing of raw data in most drugdiscovery labs (the huge forward leaps in genomics, for example, would be unthinkable without it), and this was the area where the need for automation was initially most obvious and pressing.

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