期刊
ANTIBODIES
卷 9, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/antib9020012
关键词
antibody; antigen; machine learning; deep learning; neural networks; binding prediction; protein-protein interaction; epitope mapping; drug discovery; drug design
类别
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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