Select better antibodies using high-throughput structural liability predictions 

Discover how you can predict exposed liabilities for thousands of antibodies and accelerate your candidate selection.

What will you learn?

High-throughput sequencing data improves discovery of novel therapeutic antibodies. However, getting from millions of sequences to a diverse set of developable antibodies with the right therapeutic properties can be incredibly challenging, time-consuming, and requires significant software and computational resources. In this session, you will learn how to:

  • Parse through large pools of antibody candidates generated by high-throughput sequencing
  • Integrate prediction of exposed liabilities into seamless workflows 
  • Use all assay data and in-silico predictions to select the best antibody candidates 

Speaker: Néstor Vázquez Bernat  Application Scientist at ENPICOM
As an Application Scientist at ENPICOM, Néstor focuses on analyzing customer requirements, and project setup, management, and execution. During his PhD in Immunology, he isolated and expressed monoclonal antibodies after vaccinations and developed high-throughput sequencing library preparation protocols for B cell repertoires in humans, non-human primates, and other animal models.