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Peer-Reviewed Publication
PLoS Comput Biol2021;17(9):e1009037.September 1, 2021Journal Article

XENet: Using a new graph convolution to accelerate the timeline for protein design on quantum computers.

Jack B Maguire1, Daniele Grattarola2, Vikram Khipple Mulligan3, Eugene Klyshko1,4, Hans Melo1
1Menten AI, Inc., Palo Alto, California, United States of America.
2Faculty of Informatics, Università della Svizzera italiana, Lugano, Switzerland.
3Center for Computational Biology, Flatiron Institute, New York, New York, United States of America.
4Department of Physics, University of Toronto, Toronto, Ontario, Canada.
PubMed PMC DOI PDF

Abstract

Graph representations are traditionally used to represent protein structures in sequence design protocols in which the protein backbone conformation is known. This infrequently extends to machine learning projects: existing graph convolution algorithms have shortcomings when representing protein environments. One reason for this is the lack of emphasis on edge attributes during massage-passing ope…

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