Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs)
are tremendous, the design and discovery of new candidates remain a time and cost …

Antibodies are versatile molecular binders with an established and growing role as
therapeutics. Computational approaches to develo** and designing these molecules are …

Generative machine learning (ML) has been postulated to become a major driver in the
computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to …

Beyond potency, a good developability profile is a key attribute of a biological drug.
Selecting and screening for such attributes early in the drug development process can save …

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

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 …

High-affinity antibodies are often identified through directed evolution, which may require
many iterations of mutagenesis and selection to find an optimal candidate. Deep learning …

The recombination between immunoglobulin (IG) gene segments determines an individual's
naïve antibody repertoire and, consequently,(auto) antigen recognition. Emerging evidence …

The antibody drug field has continually sought improvements to methods for candidate
discovery and engineering. Historically, most such methods have been laboratory-based …

This perspective sheds light on the transformative impact of recent computational
advancements in the field of protein therapeutics, with a particular focus on the design and …