SC13: Artificial Intelligence and Machine Learning in Drug Discovery and Development – Part 2



This second of a two-part series builds on the information offered in the first part while focusing on the applications of AI/ML further downstream in the drug development pipeline, such as for predicting adverse events and drug-related toxicities and for translational and clinical research. The challenges of visualizing and classifying large datasets and using those datasets for creating models and algorithms that can predict drug response, help patient stratification and increase the chances of success in the clinic will be discussed. This course also offers an opportunity for discussing and sharing ideas in an informal and interactive setting with leading experts in the field.


  • Brief introduction to AI
  • AI for clinical trial design: Case study in epilepsy
  • AI for patient monitoring during trials: Case study in oncology
  • AI and data for clinical trials: Challenges and entry barriers
  • Evaluating AI/ML/data analytics-focused technologies
  • Challenges faced when integrating data (clinical/genomic) for AI/ML analysis
  • Problems associated with integrating data from different sources- EMR, and clinical trials (genomic, proteomic, imaging)
  • Examples of ML algorithms that can be used in data analysis
  • Future direction in clinical trial data analysis (knowledge graph and more)
  • How to identify disparate data sources and methods for integration
  • New opportunities and challenges for model development and validation
  • Proof of concept: using cloud-based analytics solution to accelerate drug discovery


5:30 pm Course Introduction and Overview of Topic

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

5:50 Introduction to AI/ML and Applications for Clinical Trials

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

6:30 Dinner Buffet

7:00 AI/ML for Healthcare Data Analysis

Shruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine

7:30 Accelerating Drug Discovery with Analytics on the Cloud

Kuan-Fu Ding, MSc, PhD, Chief Science Officer, Catalytic Data Science

8:00 Open Discussion and Summary of Key Takeaways

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

8:30 End of course



Leibman_MichaelMichael 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.

Harrer_StefanStefan 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.

Bharadwaj_ShruthiShruthi Bharadwaj, PhD, Senior Scientist, Novartis Oncology Precision Medicine

Shruthi Bharadwaj is a Senior Scientist at Novartis within the Informatics and Analytics group. She received her PhD in Biomedical Engineering from the University of Florida and continued her research as a post-doctoral fellow at the MD Anderson Cancer Center. Shruthi has been interested and involved in utilizing AI and machine learning approaches in Pharma. She has a patent that involves machine-learning approach to predict the onset of colon cancer in patients with Inflammatory Bowel Disease. She has won several NIH grants that supported her research in leveraging AI approaches in healthcare. She has published several research articles, book chapters and abstracts that focus on AI approaches in diagnosis and drug development.

Ding_Kuan-FuKuan-Fu Ding, MSc, PhD, Chief Science Officer, Catalytic Data Science

Kuan-Fu Ding is currently the Chief Science Officer at Catalytic Data Science. Until recently he was the Chief Science Officer at Cubismi Inc. Earlier, as Chief Science Officer of Sapiens Data Science, Kuan led all aspects of the company’s science-related research, development, and solutions, including product strategy, bioinformatic and data analysis workflows, and technical support for commercial and operational functions. He worked closely with other company leaders to ensure effective use of diverse data sources, cost-effectiveness, and continuous improvement to achieve overall company success. Prior to joining Sapiens, Kuan was a Senior Data Scientist at Intrexon, where he pioneered data science and computational biology efforts in the health therapeutics division.  He successfully created a scientific team dedicated to the application of bioinformatics, machine learning, and artificial intelligence algorithms in health. Kuan received a PhD in Bioinformatics and Systems Biology from the University of California, San Diego, a MSc in Biostatistics from the University of Virginia, and a BSc in Mathematics from the University of Texas at Austin.

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