Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity ant...
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Therapeutic antibodies are an important and rapidly growing drug modality. However, the design and discovery of early-stage antibody therapeutics remain a time and cost-intensive endeavor. Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments (s...
Alternative Titles
Full title
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_4b310d77320b4b48bba9cd5498b95743
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4b310d77320b4b48bba9cd5498b95743
Other Identifiers
ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-023-39022-2
