Pre-Conference Workshop Day

Tuesday, September 14

Workshop A: Discovering & Developing Protein & Peptide Based Drug Therapies through Computational Tools

10.00 - 12.00 EDT

This interactive and hands on workshop will introduce several computational tools for developing protein and peptide based drug therapies. Case studies will demonstrate the application of these tools, explore how these can be employed and detail the applications of algorithms. Here we will delve into the future uses of protein and peptide design in the biopharmaceutical setting.

Attendees will discuss:

  • How to apply state of the art knowledge for computational peptide design
  • Computational design of peptides from small scaffolds up to redesign engineering of proteins using computational methods
  • Compound design and applications of algorithms to design
  • The future of protein and peptide design

Workshop Leaders:

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Per Jr. Greisen
Director, Computational Drug Discovery
Novo Nordisk

Dan Sindhikara
Principal Scientist, Chemistry
Merck

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Kristine Deibler
Scientist II
Novo Nordisk

Workshop B: Applications of Machine Learning in Drug
Discovery & Development Immunogenicity Prediction & Validation

13.00 -15.00 EDT

This interactive and hands on workshop will provide a set of tools to tools to improve discovery and decision making for well-specified questions with abundant, high-quality data. Featuring 2 case studies from Pfizer and Deep Biologics, we will detail how to apply machine learning in all stages of drug discovery including target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials; and antibody optimization. Here we will explore and solve challenges including lack of interpretability and repeatability of ML-generated results to prevent application limit.

Case Study A: Applications of Machine Learning for Antibody Optimization

The following subtopics will be covered:

  • Applications of machine learning for predicting antigen specificity from massively diverse libraries
  • Filtering of the identified leads for viscosity, clearance, solubility and immunogenicity
  • Experimental validation of the in silico optimized hits and testing whether they retain specificity

In the second half of this workshop, Peter from Pfizer will lead a discussion around:

  • Tools to improve discovery and decision making
  • Machine learning applications in all stages of drug discovery
  • Solving challenges in lack of interoperability and repeatability

Workshop Leaders:

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Peter Henstock
Machine Learning & AI Technical Lead
Pfizer

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Dea Shahinas
CEO
Deep Biologics