A Review of Deep Learning Methods for Antibodies

By Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. Parish, B. Medellin, Monica Berrondo. 2020

84 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Ron O. Dror

Citations in the corpus

Learning from Protein Structure with Geometric Vector Perceptrons (2020, background): “Thanks to their importance and difficulty, such problems, which we broadly refer to as learning from structure, have recently developed into an exciting and promising application area for deep learning (Graves et al., 2020; Ingraham et al., 2019; Pereira et al., 2016; Townshend et al., 2019; Won et ”