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filingDate 2020-04-08-04:00^^<http://www.w3.org/2001/XMLSchema#date>
inventor http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_383c78365bf983ecb2db777f7775bca1
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publicationDate 2020-10-15-04:00^^<http://www.w3.org/2001/XMLSchema#date>
publicationNumber WO-2020208555-A1
titleOfInvention Systems and methods to classify antibodies
abstract The present disclosure describes systems and methods to make predictions classifying one or more properties of a binding protein such as an antibody, for example, antibody affinity or specificity for an antigen. The system can include one or more machine learning models that can extrapolate complex relationships between amino acid sequence and function. The system can be trained on high-quality training data generated through a two-step single-site and combinatorial deep mutational scanning approach. The trained models can then make predictions on novel variant sequences generated in silico. The present disclosure describes amino acid sequences generated by the systems and methods provided, and uses of the generated sequences to produce proteins for therapeutic and diagnostic use.
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