A Review of Deep Learning Methods for Antibodies
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Antibodies 2020, 9(2), 12; https://doi.org/10.3390/antib9020012
Received: 1 April 2020
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Revised: 15 April 2020
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Accepted: 16 April 2020
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Published: 28 April 2020
(This article belongs to the Collection Computational Antibody and Antigen Design)
Driven by its successes across domains such as computer vision and natural language processing, deep learning has recently entered the field of biology by aiding in cellular image classification, finding genomic connections, and advancing drug discovery. In drug discovery and protein engineering, a major goal is to design a molecule that will perform a useful function as a therapeutic drug. Typically, the focus has been on small molecules, but new approaches have been developed to apply these same principles of deep learning to biologics, such as antibodies. Here we give a brief background of deep learning as it applies to antibody drug development, and an in-depth explanation of several deep learning algorithms that have been proposed to solve aspects of both protein design in general, and antibody design in particular.
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Keywords:
antibody; antigen; machine learning; deep learning; neural networks; binding prediction; protein–protein interaction; epitope mapping; drug discovery; drug design
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MDPI and ACS Style
Graves, J.; Byerly, J.; Priego, E.; Makkapati, N.; Parish, S.V.; Medellin, B.; Berrondo, M. A Review of Deep Learning Methods for Antibodies. Antibodies 2020, 9, 12. https://doi.org/10.3390/antib9020012
AMA Style
Graves J, Byerly J, Priego E, Makkapati N, Parish SV, Medellin B, Berrondo M. A Review of Deep Learning Methods for Antibodies. Antibodies. 2020; 9(2):12. https://doi.org/10.3390/antib9020012
Chicago/Turabian StyleGraves, Jordan, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. V. Parish, Brenda Medellin, and Monica Berrondo. 2020. "A Review of Deep Learning Methods for Antibodies" Antibodies 9, no. 2: 12. https://doi.org/10.3390/antib9020012
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