Data science-driven autonomous reaction optimization by UBC, Merck Co., Inc. and Chemspeed
Data driven high-throughput experimentation is enabling accelerated screening within pharmaceutical companies. New data science tools combined with machine learning are being implemented to efficiently tackle multivariate reaction optimization challenges.
Melodie Christensen, from Merck & Co., Inc., and UBC provide an overview of the use of automation in her Data Rich Experimentation (DRE) lab and her move towards autonomous reaction screening in conjunction with digitalization.
Melodie is an Associate Principal Scientist, Merck & Co., Inc. and a Ph.D. student at the Department of Chemistry, the University of British Columbia.
She has a proven track record in high-throughput experimentation platforms to support early and late stage pharmaceutical process development.
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For details please contact [email protected]