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Ep.11 Drug Discovery and How To Monetize Predictions with Stephen MacKinnon

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Guest Biography, Stephen MacKinnon

Stephen has 15+ Years of experience in Data Science & engineering ML systems. He develops large-scale, predictive approaches in drug discovery, specializing in the development of complex abstractions of proteins and biological information, suitable for data/feature engineering and advanced ML approaches.


Stephen leads the research and development of new predictive technologies at Cyclica. He coordinates the integration of ML and biophysical tools to create effective AI solutions to complex problems in drug discovery. Stephen received a Ph.D. in Computational Biochemistry, where he developed biophysical abstractions and classifications schemas to describe 3D protein interactions.

LinkedIn> https://www.linkedin.com/in/stephen-mackinnon/


Episode Highlights

  • The amazing work being performed by Cyclica in creating an AI-driven drug discovery platform
  • The evolution of artificial intelligence-driven life science research, from the rise of computational biochemistry and the dramatic expected impact on human life in the coming years
  • The convergence of AI, big data, and classic “lab bench” biochemistry, driven by the increasing number and complexity of experiments being performed
  • The new generation of Biotech “platform” companies that in some ways resemble software platform companies like Airbnb or Unity by creating a “sandbox” where others can innovate the next generation of therapies
  • The “democratization” of computational biology, where students and scientists don’t just create basic research but applied therapeutics, increasingly underserved regions of the world.
  • How biotechnology firms can partner with universities and government agencies to create effective, collaborative research. 

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    This episode was fully transcribed and can be found at patsnap.com/ep11

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