Title - 3D-Equivariant Graph Neural Networks and Transformers for Refining and Evaluating Protein Structures
Deep learning is revolutionizing the prediction of protein structure and is close to solving this grand challenge hanging over the scientific world for many years. In this talk, Dr. Cheng will describe how this artificial intelligence (AI) technology emerged in the field, how it overcame various technical hurdles to reach a high accuracy of predicting protein structures as demonstrated by AlphaFold2, and where it is going now. Dr. Cheng will present his latest work of applying 3D-equivariant graph neural networks and transformers to refine and evaluate protein structural models. The experiments demonstrate that 3D-equivariant graph network networks and transformers that are robust against the rotation and translation of 3D objects can evaluate and improve the quality of protein structures more effectively than the existing methods.
Dr. Jianlin Cheng is the Thompson Distinguished Professor in the Department of Electrical Engineering and Computer Science Department at the University of Missouri - Columbia, USA. He earned his Ph.D. in Computer Science from the University of California, Irvine in 2006. His research is focused on bioinformatics and machine learning.
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Meeting ID: 964 4412 7305