Robert, Philippe A.Akbar, RahmadFrank, RobertPavlović, MilenaWidrich, MichaelSnapkov, IgorSlabodkin, AndreiChernigovskaya, MariaScheffer, LonnekeSmorodina, EvaRawat, PuneetMehta, Brij BhushanVu, Mai HaMathisen, Ingvild FrøbergPrósz, AurélAbram, KrzysztofOlar, AlexMiho, EnkelejdaHaug, Dag Trygve TryslewLund-Johansen, FridtjofHochreiter, SeppHaff, Ingrid HobækKlambauer, GünterSandve, Geir KjetilGreiff, Victor2024-08-132024-08-132022-12-192662-845710.1038/s43588-022-00372-4https://irf.fhnw.ch/handle/11654/46938Machine learning (ML) is a key technology for accurate prediction of antibody–antigen binding. Two orthogonal problems hinder the application of ML to antibody-specificity prediction and the benchmarking thereof: the lack of a unified ML formalization of immunological antibody-specificity prediction problems and the unavailability of large-scale synthetic datasets to benchmark real-world relevant ML methods and dataset design. Here we developed the Absolut! software suite that enables parameter-based unconstrained generation of synthetic lattice-based three-dimensional antibody–antigen-binding structures with ground-truth access to conformational paratope, epitope and affinity. We formalized common immunological antibody-specificity prediction problems as ML tasks and confirmed that for both sequence- and structure-based tasks, accuracy-based rankings of ML methods trained on experimental data hold for ML methods trained on Absolut!-generated data. The Absolut! framework has the potential to enable real-world relevant development and benchmarking of ML strategies for biotherapeutics design.enMachine learningDeep learningAntibodyAntigenSpecificityEpitopeParatopeStructureReal-world600 - Technik, Medizin, angewandte Wissenschaften01A - Beitrag in wissenschaftlicher Zeitschrift845-865