August 14, 2024
Generative Chemistry, Deep Learning and Traditional Models: Practical Insights and Emerging Technologies
Look back as Eric Gifford, PhD moderated a discussion with Pat Walters, PhD, Greg Landrum, PhD and Peter Gedeck, PhD, who shared their insights, experiences and visions for the future of machine learning in drug discovery. They discussed the hype surrounding AI; what they think is worthy of the hype, what is overhyped, and what has potential moving forward. This webinar offered clarity amidst the buzz of machine learning in drug discovery.
Recorded live September 26, 2024
Moderated By...
Eric Gifford, PhD
Business Development Consultant, Collaborative Drug Discovery
Featuring these leading scientists...
Pat Walters, PhD
Chief Data Officer, Relay Therapeutics
Pat Walters is Chief Data Officer at Relay Therapeutics in Cambridge, MA. Prior to joining Relay, he spent more than 20 years at Vertex Pharmaceuticals where he was Global Head of Modeling & Informatics. He received his Ph.D. in Organic Chemistry from the University of Arizona where he studied the application of artificial intelligence in conformational analysis. Prior to obtaining his Ph.D., Pat worked at Varian Instruments as both a chemist and a software developer. He received his B.S. in Chemistry from the University of California, Santa Barbara.
Greg Landrum, PhD
Senior Scientist at ETH Zürich
Greg Landrum is a senior scientist at ETH Zürich. Prior to that, he designed and led the implementation of RDKit, and invented new algorithms for machine learning, descriptor calculation and library design. He also worked as the Global Head of Chemical Information Systems with Novartis. He received his PhD in chemistry from Cornell University.
Peter Gedeck, PhD
Research Informatics Senior Scientist, Collaborative Drug Discovery
Peter Gedeck holds a Ph.D. in chemistry. He is a scientist in the research informatics team at Collaborative Drug Discovery and develops novel technologies to be incorporated into CDD Vault. His research interests include the application of statistical and machine learning methods to problems in drug discovery. Prior to CDD, he worked for twenty years as a computational chemist in drug discovery at Novartis in the United Kingdom, Switzerland, and Singapore. Peter also teaches at University of Virginia's School of Data Science. Peter’s specialty is the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. His scientific work is published in more than 50 peer reviewed articles and five books.