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Effective management and analysis of electronic data has become a crucial determinant of success or failure
In pursuit of new drugs, pharmaceutical and biotechnology companies are today generating staggering amounts of data through the application of technologies such as high-throughput screening and parallel chemical synthesis. While data management systems are able to cope with the volume of data, serious challenges are presented by the varying data types and richer information coming from genetic, microarray, proteomic, toxicology, biological screening and chemistry technologies. Add to this data complexities arising from the industry's mergers and acquisitions, the distributed nature of the data and resources that are knowledgeable about the data across multi-national organizations, and the business need for greater efficiency, faster delivery and transparency of data.
There is no doubt that electronic data has become a key asset, if not the key asset, for pharmaceutical and biotechnology companies, and that effective management, access and analysis of electronic data is a crucial determinant of an organization's future success or failure. The majority of drug discovery information, even today, is housed within proprietary, transactional database systems using a variety of data formats and supplied by various vendors including Oracle, Daylight Chemical Information Systems, Accelrys, CambridgeSoft and Elsevier MDL. These systems are typically focused on a particular scientific silo, e.g. chemical structure management, or bioassay results management, and typically do not address the pervasive need for global data access and integration across scientific disciplines. Even within a single organization it is not unusual to find that chemical structures are managed using entirely different systems and business rules at various research sites.
These phenomena within pharmaceutical data management render visual data inspection, numerical and structure-activity relationship (SAR) analysis of combined chemical and biological data extremely challenging. Furthermore, the complex inter-relationships and links within discovery data and to external data sources, such as patents, commercially available databases and scientific publications can no longer be grasped by individuals or project teams, and thus strategies...