Session Abstract – PMWC 2022 Silicon Valley


The future of drug development is likely to be deeply impacted by machine learning in a multitude of ways. This panel will reflect on the past, present, and future of what has made drug discovery flourish and fail; with a special focus on the technology driving cutting-edge precision medicine.

 Session Chair Profile

M.D., Ph.D., Assistant Professor, Stanford University

Biography
Dr. Ansuman Satpathy M.D., Ph.D. in the Department of Pathology at Stanford University School of Medicine. He is a member of the Stanford Cancer Institute, the Parker Institute for Cancer Immunotherapy, the Immunology, Cancer Biology, and Biomedical Informatics Programs, Bio-X, and a faculty fellow in ChEM-H. Dr. Satpathy completed an M.D. and Ph.D. in immunology at Washington University in St. Louis, clinical residency in pathology at Stanford Hospital and Clinics, and postdoctoral training in genetics at Stanford University. Dr. Satpathy’s research group focuses on developing and applying genome-scale technologies to study fundamental properties of the immune system in health, infection, and cancer.


 Speaker Profile

Ph.D., Vice President, Genentech Inc.

Biography
Cornelis E.C.A. “Marcel” Hop is Vice-President at Genentech and supervising the DMPK department. He leads a team of about 85 scientists involved in acquisition and interpretation of ADME data in support of drug discovery and development ranging from early stage research to NDA and beyond. Before that, he was a Senior Director at Pfizer and a Senior Research Fellow at Merck and he received his Ph.D. from the University of Utrecht (The Netherlands). He has extensive experience in ADME sciences with a particular focus on PK optimization, human PK prediction, biotransformation, bioanalysis and the use of in silico approaches in drug discovery.


 Speaker Profile

Ph.D., CEO, Immunai

Biography
Noam is the CEO and Co-Founder at immunai, the first and only company to map the entire immune system for better detection, diagnosis, and treatment of disease. Leveraging single-cell technologies and machine learning algorithms, immunai has mapped out thousands of immune cells and their functions, building the largest proprietary data set in the world for clinical immunological data. Prior to co-founding immunai, Noam had a dual career in both the industry and academia. Noam is a double PhD, and served as a post-doctoral researcher in the Mathematics department at MIT, and in the center of mathematical sciences and applications at Harvard University. In his research, he developed and applied tools from algebra and Algebraic Geometry in the study of classical problems in combinatorics. Noam also worked as an algorithms developer in the Israeli defense forces and subsequently as a data scientist, consultant and head of data science in several hi-tech companies in Israel.


 Speaker Profile

Ph.D., Chief Strategy Officer and Head of Drug Discovery, BioSymetrics

Biography
Stacie is a pharmaceutical R&D and biotech leader with expertise in AI and machine learning applications for drug discovery. At BioSymetrics she guides the company on its drug discovery and partnering strategies, with a focus on translating human-relevant disease biology. Prior to BioSymetrics, Stacie led collaborations and partnerships in the AI-powered drug discovery space as Vice President and Head of AI Molecular Screening Partnerships at Atomwise. Previously she established and co-led the Accelerating Therapeutics for Opportunities in Medicine (ATOM) consortium, a public-private partnership developing machine learning tools to accelerate drug discovery. She worked at GSK for more than 13 years, starting as a process chemist and moving up into R&D strategy and operations roles where she led several change initiatives and teams. Stacie holds a B.S. in Chemistry from University of California Berkeley and a Ph.D. in chemistry from University of California Irvine.