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Antibody generation requires the use of one or more time-consuming methods, namely animal immunization, and in vitro display technologies. However, the recent availability of large amounts of antibody sequence and structural data in the public domain along with the advent of generative deep learning algorithms raises the possibility of computationally generating novel antibody sequences with desirable developability attributes. Here, we describe a deep learning model for computationally generating libraries of highly human antibody variable regions whose intrinsic physicochemical properties resemble those of the variable regions of the marketed antibody-based biotherapeutics (medicine-likeness).

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Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany (S.A.P., I.Q., D. Arifaj, M.K., D. Argov, L.C.R., J.S.).

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Adeno-associated viral (AAV) vectors are increasingly used for preclinical and clinical cardiac gene therapy approaches. However, gene transfer to cardiomyocytes poses a challenge due to differences between AAV serotypes in terms of expression efficiency and . For example, AAV9 vectors work well in rodent heart muscle cells but not in cultivated neonatal rat ventricular cardiomyocytes (NRVCMs), necessitating the use of AAV6 vectors for studies.

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