A Review of Deep Learning Methods for Antibodies

• Main Authors: Jordan Graves, Jacob Byerly, Eduardo Priego, Naren Makkapati, S. Vince Parish, Brenda Medellin, Monica Berrondo
• Format: Article
• Language: English
• Published: MDPI AG 2020-04-01
• Series: Antibodies
• Subjects: antibody; antigen; machine learning; deep learning; neural networks; binding prediction
• Online Access: https://www.mdpi.com/2073-4468/9/2/12

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.