Scientific presentation

Data-driven discovery made easy, scalable, and ready for the ML era

NGS and high-throughput screening and characterization tools have become mainstream in antibody discovery. These developments are rapidly generating increasing amounts of valuable data, that many research teams are still struggling trying to fully leverage. It’s essential to have solutions that enable scientists to manage and interpret these datasets in an easy, scalable, and integrated manner. In fact, these approaches are even more crucial now as researchers explore exciting new Machine Learning (ML) applications.

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
PEGS Europe.
Headshot of Piotr van Rijssel.
Piotr van Rijssel

Application Scientist
Piotr van Rijssel is an Application Scientist at ENPICOM where he helps develop tailored workflows to help life scientists maximize their insights and accelerate the development of new immunotherapeutics and vaccines. Piotr focuses on providing technical consultations and ensuring the successful application of ENPICOM’s solutions to customers’ research.

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