Deep learning-based design and experimental validation of a medicine-like human antibody library.

ABSTRACT: 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). We generated 100000 variable region sequences of antigen-agnostic human antibodies belonging to

SUBMITTER: Rajagopal N 

PROVIDER: S-EPMC11757908 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature