Deep neural-network-based language models (LMs) are increasingly applied to large-scale
protein sequence data to predict protein function. However, being largely black-box models …

Apparent parallels between natural language and antibody sequences have led to a surge
in deep language models applied to antibody sequences for predicting cognate antigen …

Antibodies are multimeric proteins capable of highly specific molecular recognition. The
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …

Biologics, such as antibodies and enzymes, are crucial in research, biotechnology,
diagnostics, and therapeutics. Often, biologics with suitable functionality are discovered, but …

While machine learning (ML) has made significant contributions to the biopharmaceutical
field, its applications are still in the early stages in terms of providing direct support for quality …

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers
from high-dimensional molecular data. However, a variety of factors related to experimental …

Recent advances in deep generative methods have allowed antibody sequence and
structure co-design. This study addresses the challenge of tailoring the highly variable …

Monoclonal antibodies have emerged as a key class of therapeutics, and nanobodies are
rapidly increasing in popularity following the approval of the first nanobody drug in 2019, yet …

High-throughput sequencing (HTS) offers a modern, fast, and explorative solution to unveil
the full potential of display techniques, like antibody phage display, in molecular biology …

Antibody-antigen specificity is engendered and refined through a number of complex B cell
processes, including germline gene recombination and somatic hypermutation. Here, we …