Antibody complementarity determining region design using high-capacity machine learning | Zendy

Ge Liu | Zendy; Haoyang Zeng | Zendy; Jonas Mueller | Zendy; Brandon Carter | Zendy; Ziheng Wang | Zendy; Jonas Schilz | Zendy; Geraldine Horny | Zendy; Michael E. Birnbaum | Zendy; Stefan Ewert | Zendy; David K. Gifford | Zendy

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Antibody 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) - complementarity (molecular biology) , computer science , artificial intelligence , antibody , immunology , biology , 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.

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