AI And Therapeutic Antibody Design

Artificial intelligence (AI) is beginning to change how new drugs are designed. One of its most promising uses is improving therapeutic antibodies, proteins that recognize specific targets in the body and are already used to treat cancer, autoimmune diseases and infections.

Finding a useful antibody, however, remains largely an experimental process. An antibody must bind strongly to its target, but it must also have the physical properties needed to become a medicine. AI could potentially make this process faster by identifying the most promising antibody sequences before they are made and tested in the laboratory.

A new study tested how well current AI methods can design antibodies. In a blinded competition called AIntibody, 29 organizations submitted 511 AI-designed or AI-selected antibodies. The antibodies were then made and tested experimentally, with the results showing that AI was most successful when it was asked to improve an antibody that already had useful properties. It was much less reliable when asked to identify the best antibody or design one from scratch.

Putting AI Predictions to the Test

The AIntibody competition tested three different stages of antibody discovery. In the first, AI models were asked to improve upon an existing antibody. In the second, they had to identify the strongest antibodies from groups of related candidates. In the third, they had to design new antibody sequences that had not appeared in the original experimental dataset. All of the antibodies were tested against the same target, a protein from SARS-CoV-2.

AI Can Improve Existing Antibodies

The clearest success came when AI was asked to improve an existing antibody. About 13% of submissions produced antibodies with at least a 20-fold improvement in binding strength while remaining developable.