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Lab Data Automation, Optimizing R&D Workflows and Lead Discovery from Biologically Complex Bioassays

With an increasing amount of data being generated today, pharma and biotech companies need to develop new technologies and strategies in order to structure, analyse and integrate this big data. If you weren’t able to attend our Global Pharma R&D Informatics & AI Congress, we have made these slides from Ralph Haffner, Dana Caulder and Pierre Farmer available.

Boosting the Data Flow by Automating Decision Points
  Ralph Haffner asks is Pharma research un-automatable? Despite the progress in technologies like IoT and AI, research labs are very often dominated by manual workflows. Having a lab automation layer which includes intelligent decision algorithms could result in increased productivity.

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Optimizing R&D workflows to enable scientific discovery
  Drug discovery and development workflows are complex and require the cooperation and coordination of numerous groups to complete routine experiments and tasks. Dana Caulder describes her team at Genentech’s approach to simplifying getting work done in an R&D environment.

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AI empowers lead discovery from biologically complex bioassays
Assessing relevant biology during drug screening is a key factor in the success of a screening campaign. Pierre Farmer shows how imaging techniques and their analyses can tap into relevant biology as well presenting an example where deep learning can simplify and accelerate the analytical assay development.

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Want to read more on Pharma R&D Informatics? Good news! Our resources page is packed full of slides and presentation videos, why not take a look now?

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