Executive summary: Computational methods for developing and designing antibodies use machine learning and deep learning approaches. This opens the door to new possibilities, such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. Our review provides a critical overview of the recent developments in deep learning approaches to therapeutic antibody design, with implications for fully computational antibody design.
Wiktoria Wilman, Sonia Wróbel,Weronika Bielska, Piotr Deszynski, Paweł Dudzic, Igor Jaszczyszyn, Jędrzej Kaniewski, Jakub Młokosiewicz, Anahita Rouyan, Tadeusz Satława, Sandeep Kumar, Victor Greiff and Konrad Krawczyk. Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery. Briefings in Bioinformatics, 2022, bbac267. DOI: https://doi.org/10.1093/bib/bbac267
Computational approaches to developing and designing antibodies are increasingly used to complement traditional lab-based processes. Today, in silico methods fill multiple elements of the discovery stage, from characterizing antibody-antigen interactions to identifying developability liabilities.
The increasing integration of computational protocols within pharma company pipelines will reduce the time and cost associated with therapeutic antibody development, potentially making immunotherapy more affordable to patients.
Most of the computational solutions in the field covered various statistical techniques such as homology modeling for structure prediction and z-scores for humanness annotation. The increasing availability of large-scale data on B-cell receptors and advances in machine learning-based model development open a new chapter in the field.
The application of deep learning techniques allowed researchers to tackle well-established problems (for example, structure prediction) but also created new fields such as generative models for novel antibody design).
This review covers the recent developments in computational antibody engineering, highlighting the new applications of deep learning. It presents methods that improve the previous state-of-the-art around structure prediction and humanization.
Other techniques covered introduce new concepts such as language-motivated embeddings and automated sequence generation, offering an entirely new way of designing antibody-based therapeutics computationally.