Open AccessAntibody complementarity determining region design using high-capacity machine learning
Author(s) -
Ge Liu,
Haoyang Zeng,
Jonas Mueller,
Brandon Carter,
Ziheng Wang,
Jonas Schilz,
Geraldine Horny,
Michael E. Birnbaum,
Stefan Ewert,
David K. Gifford
Publication year - 2019
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz895
Subject(s) - panning (audio) , computer science , complementarity (molecular biology) , modular design , phage display , computational biology , artificial intelligence , complementarity determining region , antibody , machine learning , biology , monoclonal antibody , programming language , immunology , lens (geology) , paleontology , zoom , genetics
The precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties.