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Join us in person for the College of Information Science colloquium brown bag series, featuring Travis Wheeler, Associate Professor, Department of Pharmacy Practice and Science, The University of Arizona.
Deep learning techniques have produced remarkable breakthroughs across a diverse space of challenging problemsᙾ. Based on such successes, it is natural to expect that deep learning methods will similarly lead to dramatic advances in the accuracy of methods for rapidly predicting the interaction potential of a target protein and a drug candidate. In this talk, Travis Wheeler discusses some advances along these lines, and work that he and other researchers are performing to gather massive new data sets to train the next generation of deep learning methods for virtual screening.
ᙾ Note: He asked an AI chat tool to create a haiku describing his research. It gave him the following ... then provided the second as a bonus:
Protein worlds unfold,
AI guides through vast landscapes,
Cures within our grasp.
Simulations dance,
Molecules in vibrant waltz,
Discovery awaits.
About Travis Wheeler
Travis Wheeler is an associate professor in the University of Arizona Department of Pharmacy Practice and Science. He earned his bachelor's degree in evolutionarybiology from the University of Arizona, then his PhD in Computer Science from U of A in 2009. He spent five years as a postdoc in the research group of Sean Eddy at HHMI Janelia Research Campus, then joined the Computer Science faculty at the University of Montana in 2014, where he remained until his recent move back to Arizona in 2022. Dr. Wheeler leads a large group whose research can be broadly described as “algorithms and machine learning approaches for computational biology,” primarily focused on applications to genomics, drug discovery and animal behavior classification.