Scientific presentation

Advances in lead discovery with high-throughput sequencing data and in-silico models

High-throughput sequencing has radically transformed therapeutic discovery, and increasingly higher-quality data is being generated. Analyses tools needed a leap forward to reap the full potential. Join us to discuss changes in the field, the acceleration of timelines, and the incoming implementation of ML-aided discovery through ENPICOM’s expertise and solutions.

Presentation focus

In this presentaion, we discuss the essential elements of modern data-driven discovery and how we incorporate them in the IGX Platform to enable your team to identify the most promising antibody leads. Furthermore, we will explore strategies to make ML-aided discovery accessible to your scientists by harnessing ENIPICOM’s expertise or your in-house ML capabilities.

  • Recent advances in data-driven antibody discovery
  • Fueling data-driven and ML-aided discovery with solid data infrastructure
  • A deep learning case study: multi-objective humanization using pLMs
Antibody Engineering & Therapeutics.
Headshot of Néstor Vázquez Bernat.
Néstor Vázquez Bernat

Application Science Team Lead

As an Application Science Team Lead at ENPICOM, Néstor focuses on analyzing customer requirements, and oversees 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.