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ABSTRACT

Driven by the adoption of next generation high-throughput technologies, the size of data created by scientific experiments is growing exponentially, placing a huge burden on R&D teams and hindering the drug discovery process. Data is siloed on individual workstations, impractical to audit (e.g. for FDA regulations) and integrity is compromised. Digitalising research processes with a unified data management and machine learning platform improves productivity exponentially: Scientists can concentrate on core research – not spending time on data handling and management. IT departments will be enabled to provide a secure and scalable IT environment to support the pressing needs of scientists. The drug discovery process is enhanced by data-driven decision making because there is increased visibility over the dynamic research processes of the entire organization. Our research has highlighted three main challenges that hold-back R&D teams who generate large scale data assets in their drug discovery processes. At Aigenpulse, we have developed solutions utilising the cutting-edge in data technologies and machine learning to challenges of biological researchers. By connecting biology and data technology, we are accelerating discovery and research which will produce the next generation of cheaper, safer and more effective therapies.

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