In Summary: Machine learning and model-based drug discovery is rapidly becoming the norm. Computational biology is now integrating 'wet' in vitro/in vivo and 'dry' insilico biology accelerating and saving time and resources, at least in the early stages of drug discovery. This panel will explore the new world of AI and Drug Discovery as a way to model potential drug effects more accurately and quickly.
Rachel has been with BenevolentAI for almost 2 years where she currently leads the application of AI to Target Identification. She has a PhD in Computational Biology from NYU applying machine learning to transcriptomic data for drug discovery applications, and a B.S. in Mathematics from the University of Houston. She also worked for 2 years as a software engineer at NASA’s Jet Propulsion Laboratory in Pasadena, California.