Computational and artificial intelligence-based methods for antibody development

Trends Pharmacol Sci. 2023 Mar;44(3):175-189. doi: 10.1016/ Epub 2023 Jan 18.


Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Keywords: antibody development; artificial intelligence; computational engineering; deep learning.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antibodies*
  • Artificial Intelligence*
  • Complementarity Determining Regions / chemistry
  • Humans
  • Machine Learning


  • Antibodies
  • Complementarity Determining Regions