Aug 18, 2022

The Power of Absci’s Platform for AI Drug Discovery Demonstrated in New Preprint Manuscript on bioRxiv

This preprint manuscript, which we will be submitting for peer review, demonstrates how we were able to deploy our AI drug discovery platform to rapidly optimize multiple parameters important to drug development for the antibody trastuzumab. Key highlights from this work include: 

  • A robust, validated workflow that combines AI models and proprietary wet lab assays to accurately predict an antibody’s binding affinity given only its amino acid sequence 
  • Investigation of “naturalness”, a metric that scores antibody variants for similarity to natural immunoglobulin repertoires, and characterization of its association with downstream outcomes related to developability and immunogenicity 
  • Simultaneous multiparameter optimization of binding affinities and naturalness parameters for a given target 

Compared to traditional antibody discovery methods, this work shows that our platform can: 

  • Explore a larger, more diverse area of antibody variants – orders of magnitude greater than experimental methods alone – which may improve the chances of finding more antibodies with better properties 
  • Potentially save resources and mitigate the risks associated with optimizing one property at the expense of another, by consolidating sequential optimization steps into one multiparameter optimization run 

Read the full manuscript here:

Please note that the preprint manuscript has not undergone peer review, the findings are provisional, and the conclusions may change.