Philip Kim | Associate Professor
University of Toronto

Philip Kim, Associate Professor, University of Toronto

Philip M. Kim is a professor at the University of Toronto at the Donnelly Centre and the Departments of Computer Science and Molecular Genetics. In his academic research, he has been developing novel machine learning methods for protein and peptide engineering and authored over 90 publications, 7 invention disclosures and 5 patent applications. He has co-founded several biotechnology companies and serves as consultant and member of the scientific advisory board for others. Before setting up his lab in 2009, he was a postdoctoral fellow at Yale University and an associate with McKinsey & Co. He holds a Ph.D. from the Artificial Intelligence Laboratory and Department of Chemistry at the Massachusetts Institute of Technology and a B.S. in Physics and Biochemistry from the University of Tuebingen.


Pre-Congress Workshops - November 27 @ 14:00

Workshop A - Antibodies: Discovery vs Computational Design

Series of presentations followed by panel discussion

Computational design of antibodies  - Dr Daniel Faissol, Principal Investigator, Center for Bioengineering, Executive Director, Predictive Design of Biologics, Lawrence Livermore National Laboratory

Discovery & development of broad-spectrum antibodies for Flu, COVID & RSV - Dr Phillip Lovanti, Sr. Director of R&D, Aridis Pharmaceuticals

Machine learning models for design of antibodies - Dr Sai Pooja Mahajan, Senior Scientist, Prescient Design, Genentech

Computational and artificial intelligence-based methods for antibody development - Dr Philip Kim, Professor, Principal Investigator, Department of Molecular Genetics, University of Toronto

How are NITAGs & HTAs thinking about novel antibody products? Richard Hughes, Partner, Epstein, Becker & Green, Professorial Lecturer in Law, The George Washington University Law School




last published: 02/Jan/24 12:15 GMT

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