A Review of Deep Learning Methods for Antibodies
| Autor: | Eduardo Priego, S. Vince Parish, Jacob Byerly, Monica Berrondo, Naren Makkapati, Jordan Graves, Brenda Medellin |
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| Rok vydání: | 2020 |
| Předmět: |
lcsh:Immunologic diseases. Allergy
0301 basic medicine drug design media_common.quotation_subject Immunology Review Field (computer science) 03 medical and health sciences antigen antibody Drug Discovery Immunology and Allergy Function (engineering) media_common 030102 biochemistry & molecular biology Artificial neural network Contextual image classification business.industry Drug discovery Deep learning deep learning neural networks Data science epitope mapping ComputingMethodologies_PATTERNRECOGNITION machine learning protein–protein interaction 030104 developmental biology Drug development Artificial intelligence lcsh:RC581-607 business binding prediction |
| Zdroj: | Antibodies Antibodies, Vol 9, Iss 12, p 12 (2020) |
| ISSN: | 2073-4468 |
| DOI: | 10.3390/antib9020012 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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