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DTSTART:20070311T020000
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DTSTAMP:20261027T130831
DTSTART;TZID=America/Detroit:20261104T160000
DTEND;TZID=America/Detroit:20261104T170000
SUMMARY:Workshop / Seminar:CCMB/DCMB Weekly Bioinformatics Seminar Series featuring Peter Toogood\, PhD (Research Associate Professor and Director of Michigan Drug Discovery (MDD))
DESCRIPTION:High throughput screening (HTS) is a common starting point for small molecule drug discovery campaigns\, especially when the biological target is new. HTS is commonly conducted in the wet lab (e.g. the U-M Center for Chemical Genomics)\, but also can be conducted in silico. These \"virtual screens\" are either structure-based\, requiring a knowledge of the 3D structure of the biological target\, or ligand-based\, in which case having multiple overlapping ligands makes it easier to define the feature set that new ligands must match. In this talk\, I will describe several applications of artificial intelligence/machine learning to virtual screening in our projects seeking to identify new starting points for cancer drug discovery. In so doing\, I will trace the evolution of these methods over the last decade from crude attempts at generative design to the application of frontier models like Claude Science.
UID:154152-21917289@events.umich.edu
URL:https://events.umich.edu/event/154152
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Artificial Intelligence,Basic Science,Bioinformatics,Drug Discovery,Gen Ai,Machine Learning
LOCATION:Medical Science Unit I - 4B700
CONTACT:
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