2019 Archived Content
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SC5: APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DISCOVERY AND DEVELOPMENT

MONDAY, SEPTEMBER 16 | 2:00 - 5:00 PM

Room Location: Essex North  

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 AI & 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.

Detailed Agenda

2:00 pm Welcome and Opening Remarks

2:10 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

2:50 Utilizing Phonemics and Machine Learning To Accelerate Drug Discovery And Development

Daniel Anderson PhD, VP, Biology, Recursion Pharma

  • Data collection and processing
  • predictive model building and validation
  • Application of predictive models to drug discovery

3:30 Coffee Break

3:45 AI-based Method for Predicting and Validating Therapeutic Peptides

Paul Rohricht MS MBA, Chief Business Officer – Pharma, Nuritas Corporation

  • AI based drug discovery is in its infancy but is quickly growing up
  • Review of Nuritas’ method for predicting and validating therapeutic peptides
  • A case study to predict and validate a TNF-α lowering peptide used to treat inflammation
  • Lessons learned and its applications to several targets relevant to pharma today

4:20 Application of Data Science, Machine Learning, and AI in 3D Models For Precision Medicine

Kuan-Fu Ding, MSc, PhD, Chief Science Officer and Chief Technology Officer, Cubismi Inc.

5:00 Close of Short Course

Instructors:

Rao_ArvindArvind Rao, PhD, Associate Professor, Department of Computational Medicine and Bioinformatics, The University of Michigan, Ann Arbor

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.

Anderson_DanielDaniel Anderson PhD, VP, Biology, Recursion Pharma

Dan earned his PhD in cell biology and biochemistry from the University of California, San Diego. He then went on to start his biotech career at Genentech. During this period, he led a group responsible for developing novel cellular and advance microscopy assays to report on drug activity and mechanism of action. Dan then joined Cleave Biosciences, a company newly formed to discover and develop drugs for novel targets in protein homeostasis. As head of biology, he contributed to various aspects of the p97 program from early screening efforts through clinical development. Dan now lead the Innovation Biology Team at Recursion. Recursion is a clinical-stage biotechnology company combining experimental biology and automation with artificial intelligence in a massively parallel system to efficiently discover potential drugs for diverse indications, including genetic disease, inflammation, immunology, and infectious disease. Recursion applies causative perturbations to human cells to generate disease models and associated biological image data. Recursion’s rich, relatable database of more than a petabyte of biological images generated in-house on the company’s robotics platform enables advanced machine learning approaches to reveal drug candidates, mechanisms of action, and potential toxicity, with the eventual goal of decoding biology and advancing new therapeutics to radically improve lives.

Rohricht_PaulPaul Rohricht MS MBA, Chief Business Officer – Pharma, Nuritas Corporation

Paul Rohricht, MS MBA, is Chief Business Officer-Pharma, for Dublin-based AI drug discovery company Nuritas, and is based in Philadelphia. Prior to Nuritas, Mr. Rohricht has held senior business development positions with early-stage med tech firms including ChemImage and Symphogen, and is the co-founder of Revivicor, which was sold to United Therapeutics, and held the position of Entrepreneur-in-Residence at Wake Forest University School of Medicine. He is a US and EU patent holder, with several others pending. He is a graduate of St. Olaf College and the Wharton School of Business.

Ding_KuanKuan-Fu Ding, MSc, PhD, Chief Science Officer and Chief Technology Officer Cubismi, Inc.

Kuan-Fu Ding is currently the Chief Science Officer and Chief Technology Officer at Cubismi, Inc. Until recently as the 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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