Training Seminars

Training Seminars Will Be Held In Person Only

To ensure a cohesive and focused learning environment, moving between conference sessions and the training seminars is not allowed

Monday, January 13, 2025 9:00 am - 6:00 pm

TS1A: Introduction to Machine Learning for Biologics Design

Detailed Agenda

This course offers an introduction to concepts, strategies, and machine learning methods used for biologics design. It includes presentations and demonstrations of the methods used in the field, covering techniques such as triaging sequences, modulating affinity, and designing antibody libraries, along with increasing manufacturability. The course is directed at scientists new to the field and protein engineers wanting an introduction to how machine learning can aid in guiding biologics design.

Christopher R. Corbeil, PhD, Research Officer, Human Health Therapeutics, National Research Council Canada

Francis Gaudreault, PhD, Associate Research Officer, Human Health Therapeutics, National Research Council Canada

Basics of machine learning and where does it fit into drug discovery
Modern homology modeling and structure prediction
Predicting antibody affinity and specificity modulation
Generative design in biologics: library design and language models
Machine learning applications of T-cell and B-cell Immunogenicity
Methods and application of ML for chemical, folding, solution stabilities

INSTRUCTOR BIOGRAPHIES:

Christopher R. Corbeil, PhD, Research Officer, Human Health Therapeutics, National Research Council Canada

Dr. Christopher Corbeil is a research officer at the National Research Council Canada (NRC) who specializes in the development and application of computational tools for biotherapeutic design and optimization. He is also an associate member of the McGill Biochemistry Department and teaches classes in Structure-Based Drug Design at McGill University. After receiving his PhD from McGill University, he joined the NRC as a Research Associate investigating the basics of protein-binding affinity. Following his time at the NRC he joined Chemical Computing Group as a research scientist developing tools for protein design, structure prediction, and binding affinity prediction. He then decided to leave private industry and rejoin NRC with a focus on antibody engineering. Dr. Corbeil has authored over 30 scientific articles and is the main developer of multiple software programs.

Francis Gaudreault, PhD, Associate Research Officer, Human Health Therapeutics, National Research Council Canada

Francis obtained his PhD in Biochemistry from University of Sherbrooke in 2015, during which he developed a molecular docking program for docking small molecules to flexible protein or RNA targets. While doing his PhD studies, Francis co-founded a successful IT company for automating the management of scientific conferences. Francis joined the National Research Council (NRC) of Canada in 2016, where he has taken part in and led various efforts in the discovery and engineering of antibodies or other biologics. In such efforts are included the structure prediction of antibodies alone or in complex, the affinity assessment of antibody-antigen complexes, and the detection of antibody developability issues. Francis is leading the technical efforts in using artificial intelligence for antibody discovery.

TS2A: Implementing Artificial Intelligence and Computational Tools in Biopharmaceutical R&D

Detailed Agenda

Artificial intelligence and other computational techniques have revolutionized biopharma research over the past two decades, leading to the solutions of fundamental problems such as protein folding and the acceleration of numerous aspects of biopharma R&D. This seminar will survey the landscape of AI and computational tools with an emphasis on the steps needed to implement AI-based workflows and programs in biotherapeutic research organizations. We will examine case studies and interactive demonstrations in a range of application areas in which AI has led to acceleration and innovation, including identifying novel drug targets, predicting protein structure, designing small molecules and antibodies, and optimizing biopharmaceutical manufacturing processes.

Ryan Peckner, PhD, Director, Machine Learning, Seismic Therapeutic

Attendees do not need a deep computational background but will be introduced to cloud computing and containerized workflows in a hands-on fashion and should be familiar with basic concepts of programming and command line operations.

Topics to be covered:

Identifying opportunities for AI/ML tools in existing and new programs
Evaluating internal staff and experimental capabilities
The role of ML scientists; do these need to be internal?
Scoping, developing and sourcing training data
Bespoke versus off-the-shelf models
Cloud computing and containerized workflows
Identifying drug targets in silico
Protein structure prediction
Antibody design and developability
Small molecule design
Designing de novo proteins with deep learning

INSTRUCTOR BIOGRAPHIES:

Ryan Peckner, PhD, Director, Machine Learning, Seismic Therapeutic

Ryan Peckner has been the head of machine learning at Seismic Therapeutic since early 2022, where he leads a team focused on applying ML to develop next-generation classes of non-immunogenic protein therapeutics. He earned his PhD in theoretical mathematics at Princeton University in 2015 and, after deciding to transition to an applied field, completed his postdoctoral training at the Broad Institute with an emphasis on the intersection of proteomics, genomics, and machine learning. Since entering biotech in early 2019, he has focused on developing and applying new machine learning techniques to structural biology, immunology, and drug development, beginning with models to probe TCR-pMHC interactions at Repertoire Immune Medicines and continuing with his work at Seismic.

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JANUARY 19 - 20

JANUARY 20 - 21

Predicting Developability and Optimization Using AI