ENPICOMDeep learning based humanization of therapeutic antibody candidates
In Part II of this webinar, we will introduce a next generation, multi-species antibody humanization service powered by ENPICOM’s novel AIGX generative deep learning model. Through case study data on anti-RAGE antibodies, we will show that GenovacAI generates humanized antibody sequences at a much greater success rate – measured by percent germline identity and retention of binding affinity – in comparison to traditional tools, including CDR grafting and publicly available machine learning algorithms. The measurably improved functionality of GenovacAI effectively enables humanization of large numbers of candidate antibodies immediately post-discovery, resulting in reduced development costs and shortened timeline to IND filing.
Meet the speaker
Piotr van Rijssel works closely with pharmaceutical partners, organizing and managing efforts to integrate their research with advanced analytical solutions. By streamlining discovery and development workflows, he supports the efficient development of antibody-based therapies.
Piotr van Rijssel
Senior Application Scientist
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