Session Abstract – PMWC 2019 Silicon Valley

Session Synopsis: The pharmaceutical industry is applying Active Learning in various areas which includes the integration of experiment and computational modeling, automation, big data analytics, and informatics. This session will focus on pharma preparations and applications of AI and Machine Learning across drug discovery and development – various examples will demonstrate how pharma is harnessing the opportunity of large data sets to predict and improve human translation in clinical studies.

Session Chair Profile

Ph.D., Co-Lead and Chief Operating Officer, GSK; COO, ATOM Consortium
Biography

Stacie Calad-Thomson is Chief Operating Officer of the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium, a new public-private partnership that aims to accelerate the discovery of effective cancer therapies through integration of high-performance computing, diverse biological data, and emerging biotechnologies. Founding consortium members include the Department of Energy’s Lawrence Livermore National Laboratory, GSK, the National Cancer Institute’s Frederick National Laboratory for Cancer Research, and the University of California, San Francisco. The goal of the consortium is to create a new paradigm of drug discovery that would reduce the time from an identified drug target to clinical candidate from the current approximately six years to just 12 months. Stacie has worked for GSK, starting as a chemist and moving into business strategy and operations roles within R&D Platform Technology & Sciences, where she led several change initiatives. She has a BS (UC Berkeley) and a PhD (UC Irvine) in chemistry.

Speaker Profile

Ph.D., Head of Artificial Intelligence, Genentech
Biography

Kim has been involved in large scale machine learning and medical informatics initiatives for over 15 years, over a range of ventures from computational drug design to disease risk prediction. He is currently the Head of the Artificial Intelligence group for Genentech, Early Clinical Development. Recently, he served as founder and Chief Data Scientist at Lumiata, a predictive health analytics company. Kim received degrees from the University of Adelaide (Science and Medicine), and a PhD from the University of Melbourne (Australia) He was a Peter Doherty fellow and received postdoctoral training at the University of Cambridge, and Stanford University (Dr Vijay Pande). He then held leadership and consulting roles in the pharmaceutical and medical informatics industry. Kim began his industry career at Vertex Pharmaceuticals (Pat Walters) Following this, Kim worked as the founding team for Discovery Engine (acquired by Twitter in 2009) and health informatics at Gliimpse (acquired by Apple in 2017). Kim currently serves on the board of OpenEye Scientific Inc.

Speaker Profile

Ph.D., MBA, Chief Data and Analytics Officer, Eli Lilly and Company
Biography

Vipin Gopal, Ph.D. Analytics executive with 20+ years of leadership experience at multiple Fortune 100 companies, ranging from health plans to industrial conglomerates. Proven track record in delivering value to a broad range of strategic and operational business problems through analytics. Authority in analytics best practices, and cross pollination of ideas for “how to best make analytics work” across industries. Experienced in building differentiating analytic competencies and high performance corporate analytic organizations throughout career.

Speaker Profile

M.D., Ph.D., VP Scientific & Medical Affairs, Molecular Health GmbH
Biography

Dr Armin Schneider has 20 years track record in leading roles in Biotech drug discovery and development and MD PhD from the University of Heidelberg, Germany. At Molecular Health, Armin pioneers the utilization of the company’s technology, Dataome® – a mighty clinical-molecular knowledge base of drugs, diseases and outcomes into machine learning applications and predictive algorithms to predict drug outcomes and R&D efficiency. He has authored over 150 publications and owns numerous patent families and has a strong background in neurology, pharmacology, molecular biology, and statistics, and specific interest in questions of applying advanced statistical methodology to drug development.

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