Abstract
Deep learning applied to antibody development is in its adolescence. Low data volumes and biological platform differences make it challenging to develop supervised models that can predict antibody behavior in actual commercial development steps. But successes in modeling general protein behaviors and early antibody models give indications of what is possible for antibodies in general, particularly since antibodies share a common fold. Meanwhile, new methods of data collection and the development of unsupervised and self-supervised deep learning methods like generative models and masked language models give the promise of rich and deep data sets and deep learning architectures for better supervised model development. Together, these move the industry toward improved developability , lower costs, and broader access of biotherapeutics .
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Shaver, J.M., Smith, J., Amimeur, T. (2022). Deep Learning in Therapeutic Antibody Development . In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_19
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