SC5: Artificial Intelligence and Machine Learning in Drug Discovery and Development – Part 1

SUNDAY, MARCH 1 | 2:00 - 5:00 PM

ABOUT THIS COURSE:

This first of a two-part series aims to educate a diverse group of scientists – primarily chemists and biologists – about the growing use and applications of artificial intelligence (AI) and machine learning (ML) for early drug discovery. Starting with an overview of current challenges and opportunities, the various talks highlight how AI/ML can help with drug design, target identification, PK/PD predictions, lead optimization and more. The instructors will offer insights into the caveats and limitations of using AI/ML, which will help separate the hope from the hype. This course offers an excellent opportunity for informal networking and brainstorming on key issues being discussed in the field.

TOPICS TO BE COVERED:

• Brief Introduction to AI/ML
• AI/ML paradigms and applications: finding patterns in data, PCA, clustering, classification
• Understanding the caveats and limitations in AI/ML: interpretability, training-test equivalence
• Single-task and multitask approaches in drug-target prediction
• AI for target identification and validation for various indications
• A perspective on application of disease signatures for target identification and validation
• How to use ML to pick quality drug targets?
• Computational methods to pick disease indications
• Can AI (over)predict clinical success?

COURSE AGENDA:

2:00 pm Course Introduction and Overview of Topic

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC

2:20 Introduction to AI/ML

Stefan Harrer, PhD, Manager and Research Staff Member, Brain-Inspired Computing, IBM Research; Adjunct Professor, School of Engineering and Information Science, University of Technology Sydney

2:45 AI/ML Paradigm and Applications

Arvind Rao, PhD, Associate Professor, Department of Computational Medicine and Bioinformatics, University of Michigan

3:15 Coffee Break

3:30 AI for Target Identification and Validation

Deepak Kumar Rajpal, PhD, Head, Bioinformatics, Translational Sciences, Sanofi

4:00 AI Applications for Preclinical Drug Discovery

Pankaj Agarwal, PhD, Chief Computational Biologist, BioInfi

4:30 Open Discussion and Summary of Key Takeaways

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC

5:00 End of course

INSTRUCTORS:

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC

Michael N. Liebman, PhD, is the Managing Director of IPQ Analytics, LLC and Strategic Medicine, Inc after serving as the Executive Director of the Windber Research Institute (now ChanSoon-Shiong Institute for Molecular Medicine) from 2003-2007. He is an Adjunct Professor of Pharmacology and Physiology at Drexel College of Medicine and Adjunct Professor of Drug Discovery, First Hospital of Wenzhou Medical University and also Fudan University. Previously, he was Director, Computational Biology and Biomedical Informatics, University of Pennsylvania Cancer Center 2000-2003. He served as Global Head of Computational Genomics, Roche Pharmaceuticals and Director, Bioinformatics and Pharmacogenomics, Wyeth Pharmaceuticals, Director of Genomics for Vysis, Inc. He is a co-founder of Prosanos, Inc (now United BioSource) (2000). He was Associate Professor of Pharmacology and of Physiology/Biophysics at Mount Sinai School of Medicine. He serves on 14 scientific advisory boards and the Board of Directors of the Nathaniel Adamczyk Foundation in Pediatric ARDS and Innovene Pharmaceuticals. Michael is Chair of the Informatics Program and also Chair of Translational Medicine and Therapeutics for the PhRMA Foundation and a member of their Scientific Advisory Board. His research focuses on computational models of disease progression that stress risk detection, disease processes and clinical pathway modeling, and disease stratification from the clinical perspective. He utilizes systems-based approaches and design thinking to represent and analyze risk/benefit analysis in pharmaceutical development and healthcare.

Stefan Harrer, PhD, Manager and Research Staff Member, Brain-Inspired Computing, IBM Research; Adjunct Professor, School of Engineering and Information Science, University of Technology Sydney

In 2015 Stefan founded the Brain-Inspired Computing Research program of IBM Research – Australia and now leads it as its Manager. His team spearheads an effort to develop AI-based technology for managing and treating epilepsy. Since joining IBM Research in 2008, Stefan has worked in the fields of biotechnology, nanotechnology and healthcare analytics at IBM Albany Nanotech, and the IBM T.J. Watson Research Center in New York as well as at IBM Research Australia. He has authored and co-authored over 40 technical publications and holds over 50 issued and over 60 pending patents. Stefan has been named IBM Master Inventor in 2017 and was elected into the IBM Academy of Technology in 2018. He holds an Adjunct Professor position in the School of Engineering and Information Science at the University of Technology Sydney. As a Member of the New York Academy of Sciences and Senior Member of the IEEE he is part of the IEEE Computer Society Steering Committee and an Associate Editor of the IEEE Transactions on Nanobioscience. Stefan has received a Research Scholarship from UC Berkeley, a Karl Chang Innovation Fund Grant from MIT, an Honorary Principal Research Fellowship from the University of Melbourne and several Research Grants from the NIH and the Australian Research Council. His work has been featured in WIRED Magazine, The World Economic Forum, ABC News, IEEE Spectrum, The Australian Academy of Science, Popular Science and R&D Magazine among others. He holds a PhD in EECS from the Technical University Munich and an Honours Master’s Degree in Technology Management from the Center for Digital Technology and Management.

Arvind Rao, PhD, Associate Professor, Department of Computational Medicine and Bioinformatics, University of Michigan

Arvind Rao is an Associate Professor in the Department of Computational Medicine and Bioinformatics at the University of Michigan. His group uses image analysis and machine learning methods to link image-derived phenotypes with genetic data, across biological scale (i.e., single cell, tissue and radiology data). Such methods have found application in radiogenomics and drug repurposing based on phenotypic screens. Arvind received his PhD in Electrical Engineering and Bioinformatics from the University of Michigan, specializing in transcriptional genomics, and was a Lane Postdoctoral Fellow at Carnegie Mellon University, specializing in bioimage informatics.

Pankaj Agarwal, PhD, Chief Computational Biologist, BioInfi

Pankaj Agarwal has 22+ years of strategic and tactical experience utilizing bioinformatics to enable drug discovery and create pipeline value.  Dr. Agarwal has 50+ publications in top journals and multiple methodological and gene patents. Dr. Agarwal has served on NSF, NIH, FDA and PhRMA panels. He possesses a B.Tech. in Computer Science & Engineering from IIT, Delhi and a Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences at NYU. He is a founding director and senior member of the International Society for Computational Biology (ISCB). GSK Pharmaceuticals R&D appointed him a Senior Fellow in recognition of his internal and external achievements during his 22+ year tenure. At BioInfi, Dr. Agarwal serves as an advisor and strategic consultant to disease foundations and biotechnology companies including the Bill & Melinda Gates Foundation. Most importantly, Pankaj is passionate about drug discovery, data-driven solutions, and helping patients.

Deepak Kumar Rajpal, PhD, Head, Bioinformatics, Translational Sciences, Sanofi

Deepak Rajpal serves as head of bioinformatics, at Sanofi, delivering translational sciences research to therapeutic areas on target identification and validation, building and progressing drug discovery pipeline, biomarker discovery and patient selection.  Prior to Sanofi, at GSK, over the past 17 years, assumed increasing responsibilities by delivering on various aspects of drug discovery and development in multiple therapy areas. Served as domain expert of computational biology for various metabolic and immune-mediated dermatological conditions. Provided strategic research partnership in building drug discovery units and established external collaborations to bring in innovative research ideas.

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