Artificial intelligence aids discovery of super tight-binding antibodies
Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs.
In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug. The authors say the pipeline could accelerate the discovery of novel drugs against cancer and other diseases such as COVID-19 and rheumatoid arthritis.
The approach starts similarly, with researchers generating an initial library of about half a million possible antibody sequences and screening them for their affinity to a specific protein target. But instead of repeating this process over and over again, they feed the dataset into a Bayesian neural network which can analyze the information and use it to predict the binding affinity of other sequences.
One particular advantage of their AI model is its ability to report the certainty of each prediction.
To validate the pipeline, project scientists and co-first authors of the study Jonathan Parkinson, PhD, and Ryan Hard, PhD, set out to design an antibody against programmed death ligand 1 (PD-L1), a protein highly expressed in cancer and the target of several commercially available anti-cancer drugs. Using this approach, they identified a novel antibody that bound to PD-L1 17 times better than atezolizumab (brand name Tecentriq), the wild-type antibody approved for clinical use by the U.S. Food and Drug Administration.
