SC16: Applications of Artificial Intelligence in Drug Discovery & Development
Room Location: 153

SUNDAY, MARCH 10, 5:30 - 8:30 PM

This course aims to educate a diverse group of scientists-chemists, biologists, toxicologists, and those involved in translational and clinical research, about the growing use and applications of artificial intelligence (AI) and machine learning (ML). Talks start with explaining the basic terminology used and what it means, followed by discussions separating the hope from the hype. It goes into the caveats and limitations in AI and ML, while exploring ways in which it can be successfully applied in the drug discovery and development pipeline. There will be experts from various areas presenting case studies on how they have used AI/ML tools for lead optimization, target discovery, visualizing and classifying large datasets, patient stratification and more.

5:30 Welcome and Speaker Introductions

5:40 Fundamentals of ML for Drug-Repurposing: Basics and Selected Case studies

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

• 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

6:20 Dinner and Dessert Break

6:45 Integrating Clinical Metadata and Genomics to Help Individuals Measure, Protect, and Improve Their Health at the Speed of Science

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

• How to identify disparate data sources and methods for integration
• New opportunities and challenges for model development and validation
• Proof of concept: using genomics to improve health assessment

7:20 Development of Machine Learning-Based Tools for Clinical Use in the Diagnostic Lab

Orly Ordon, MSc, MBA, PhD, Program Manager, Research and Development, ARUP Laboratories/University of Utah

• Description of current trends in diagnostic laboratory operations and opportunities for ML based tools
• Challenges for ML tool development in diagnostic labs
• Case study of a successful ML tool development for clinical use at ARUP Laboratories

7:55 From Big Data to Smart Data: Quantitative Systems Pharmacology Modeling for Augmented Human Intelligence

Hugo Geerts, PhD, BachMed, PharmaMBA, CSO, Computational Neuropharmacology, In Silico Biosciences Inc.

• How to develop predictive models when there is no or low-quality data as training set?
• From correlation to causation using Formalized Domain Expertise
• The virtual patient: A quantitative predictive modeling platform to support R&D from target validation and clinical trial support to rational polypharmacy in clinical practice

8:30 End of Course

SPEAKER BIOGRAPHIES

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.

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

As the Chief Science Officer of Sapiens Data Science, Inc., Kuan leads all aspects of the company’s science-related research, development, and solutions, including product strategy, bioinformatic and data analysis work flows, and technical support for commercial and operational functions. Additionally, he works 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.

Orly Ardon, MSc, MBA, PhD, Program Manager, Research and Development, ARUP Laboratories/University of Utah

Orly Ardon, PhD MBA is heading the development of novel computer assisted diagnostic tools and the expansion of digital pathology collaborative initiatives at ARUP Laboratories, a national reference lab in Salt Lake City, Utah. Orly has over thirty years of academic and life science industry experience that includes laboratory research in microbiology, genetics, biochemistry, immunology and genomics. Orly’s business experience includes US and global healthcare market research, budget and contract negotiations, team building and the development of innovative solutions to complex problems. Orly has BS, MS, and PhD degrees in Microbiology from the Hebrew University of Jerusalem, and postdoctoral training in cell biology and virology at the University of Utah. Orly joined the R&D Institute at ARUP Laboratories in 2007, where she managed a medical genetics lab and developed new commercial diagnostic tests for the biochemical genetics and molecular genetics labs at ARUP. After graduating from the Executive MBA program at the University of Utah, Orly joined the Business Development group at ARUP where she worked as an R&D market research manager for two years. In her current R&D management role, Orly combines her extensive scientific and business experience to identify and oversee the development of new machine learning based digital imaging and automation tools for state-of-the-art clinical operations and for improved patient care. Orly has authored 24 peer-reviewed manuscripts and a book chapter (cited more than 1,600 times in scientific literature).

Hugo Geerts, PhD, BachMed, PharmaMBA, CSO, Computational Neuropharmacology, In Silico Biosciences Inc.

Hugo Geerts spent 17 years in the (CNS) Drug Discovery Area, with Dr. Paul Janssen, probably the greatest drug hunters in history at the Janssen Research Foundation in Beerse, Belgium doing research in Alzheimer’s disease with targets in tangle and amyloid pathology. He was involved in supporting the successful preclinical, clinical and postmarketing development of galantamine for Alzheimer’s Disease. In 2002, he co-founded In Silico Biosciences, a company providing mathematical modeling of pathological interactions in the brain for supporting the whole process of Drug Discovery – from Target validation to clinical trial design - in Psychiatry and Neurology, an approach now called Quantitative Systems Pharmacology. He is leading the Brain Health Modeling Initiative, is a driving force behind the NINDS/NIA workgroup on QSP in CNS and is co-chairman of the QSO workgroup in the International Society for Pharmacometrics. He has over 90 peer-reviewed publications and patents.

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