A Review of Deep Learning Methods for Antibodies

Antibodies (Basel). 2020 Apr 28;9(2):12. doi: 10.3390/antib9020012.


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.

Keywords: antibody; antigen; binding prediction; deep learning; drug design; drug discovery; epitope mapping; machine learning; neural networks; protein–protein interaction.

Publication types

  • Review