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 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.

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.