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Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction

Philippe A. Robert, Rahmad Akbar, Robert Frank, Milena Pavlović, Michael Widrich, Igor Snapkov, Andrei Slabodkin, Maria Chernigovskaya, Lonneke Scheffer, Eva Smorodina, Puneet Rawat, Brij Bhushan Mehta, Mai Ha Vu, Ingvild Frøberg Mathisen, Aurél Prósz, Krzysztof Jan Abram, Alex Olar, Enkelejda Miho, Dag Trygve Tryslew Haug, Fridtjof Lund‐Johansen, Sepp Hochreiter, Ingrid Hobæk Haff, Günter Klambauer, Geir Kjetil Sandve, Victor Greiff

2022Nature Computational Science56 citationsDOI

Topics & Concepts

BenchmarkingComputer scienceUnavailabilityBenchmark (surveying)Machine learningArtificial intelligenceParatopeEpitopeAntibodyData miningImmunologyBiologyEngineeringGeographyReliability engineeringMarketingGeodesyBusinessMonoclonal and Polyclonal Antibodies Researchvaccines and immunoinformatics approachesGlycosylation and Glycoproteins Research
Unconstrained generation of synthetic antibody–antigen structures to guide machine learning methodology for antibody specificity prediction | Litcius