The engagement provided data analysis consultancy to support a global pharmaceuticals company's AI Powered Drug Development project, focusing on data integration, digital twin development, and enabling AI-ready data.
Background
The AI Powered Drug Development project was a five-year capital programme aimed at improving the client's PT&D data landscape to enable AI-ready data, establish digital twins, and deliver GenAI applications to drive efficiency across drug development activities.
The Challenge
The engagement needed to support development of an integrated and accessible data ecosystem by connecting multiple data sources, including Electronic Lab Notebooks, local storage, and Scientific Computing Platform systems. It required identifying and integrating data sources to create a unified materials database, aligning data solutions with FAIR principles, and ensuring interoperability across systems and scientific domains. The role also involved enabling connectivity between experimental and predicted data, supporting development of digital twins, and coordinating across scientific, IT, and digital teams to align requirements and delivery.
Our Approach
B2E mapped connectivity between multiple data sources, including Electronic Lab Notebooks, local storage, and Scientific Computing Platform systems, to support development of a unified data ecosystem. The team identified and integrated data sources to support creation of an accessible materials database and collaborated with scientific communities to ensure alignment across domains, liaising between scientists, IT, and digital teams to connect requirements with IT work planning and related initiatives. B2E identified and implemented opportunities to connect models and establish digital twins, leveraging automation workflows to streamline data processing and analysis, and ensured alignment with data standards and FAIR principles to maximise data accessibility and usability.
Results
The engagement supported development of an integrated data ecosystem and enabled progress towards AI-ready data capabilities within the client's PT&D function. It facilitated improved data accessibility, interoperability, and efficiency in data processing and analysis through integration of multiple data sources and digital twin development.
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