7:45 AM - 8:45 AM

Registration and Breakfast in the AI Café

8:45 AM - 9:00 AM

Chair's Opening Remarks: Where You Are Headed, You Can't Go It Alone

In this opening session, Harry will set the stage for the discussions that will take place over the course of the Summit and identify the key questions that need to be asked and answered. What organizational structures will produce the best results? Should you partner or make acquisitions? How do you best structure post-acquisition assets? How do you make the cultural shift to change workflows around the technology to get real benefit for the AI tools, instead of trying to fit technology into existing workflows? And most importantly, how do you identify the problems the technology is best suited to solve? Wherever you are on the road to AI adoption, you can’t go it alone so join the conversation and help drive the movement forward.

9:00 AM - 9:40 AM

Real Talk: An open discussion on the Urgency to Advance AI Applications in Industry to Bring Benefits to Patients Faster

AI is billed as a transformational technology for biopharma.  As the use of AI, Machine Learning, and Advance Analytic tools increase it’s important to take stock and evaluate what are the successful applications and understand where biopharma as an industry is with applying AI tools. Join the conversation with:



AI in R&D

9:40 AM - 10:05 AM

Achieving Near Term ROI using AI and Machine Learning in Life Sciences R&D

Having been burnt many times in the past, the life science industry is moving cautiously in its adoption of AI and machine learning technologies.  Beyond comprehensive validation, leading innovators in the industry are also requiring clear, quantifiable benefits from AI solutions.  In this session, we hope to highlight the spectrum of AI opportunities, measured progress to date, and opportunities to systematically move forward.

10:05 AM - 10:35 AM

AI Applications for Identifying New Drug Candidates

Your CEO knows the company needs to adopt AI/ML technology, but they don't want to get involved in the details.  You now have the budget, but you need to produce an ROI.  What are your options when it comes to using AI tools to identify new drug candidates?  AI has the potential to become an indispensable tool but only if the near-term ROI is realized.  What questions do you need to ask to get this right and make a difference in speed, cost and outcomes?



  • Adam West

    Adam West Director, Research Data Sciences and Engineering Eli Lilly and Company

10:35 AM - 11:05 AM

Harnessing the Power of AI to Generate Novel Drug Candidates Using Patient-Driven Biology

The opportunity to improve health outcomes by using a collaborative and AI-enabled targeted approach to drug discovery and development is upon us.  In this session, Niven will address the biggest challenges still ahead that the healthcare community needs to address and what can be accomplished by starting and ending with patient-driven biology.

11:05 AM - 11:35 AM

Networking Refreshments & Expert One-to-One Meetings

AI Café

11:35 AM - 11:50 AM

Deploying AI Tools to Accelerate Each Step in the Drug Discovery Pipeline

While we may still be a decade away from fully in silico drug discovery, AI has proven to have vast potential in this area.  Expanding and accelerating traditional approaches like phenotypic screening provide a feasible near-term solution to bringing substantial improvements to the efficiency of discovery and development efforts.  This session will describe technical strategies to accelerate discovery using AI, including an image-based phenotypic screening platform.  The use of deep learning models to build predictive tools for multiple stages in the drug discovery pipeline will also be discussed.

11:50 AM - 12:05 PM

Developing a Large-Scale and Hypothesis-Free Drug Discovery Model for Rare Diseases

Every rare disease deserves a treatment. To address this challenge, Healx developed a scalable and hypothesis-free drug discovery model. Integrating patient group expertise and multi-omic data with an AI-based platform enables novel ways of identifying treatment opportunities for rare diseases. This session will discuss a large-scale, parallel and hypothesis-free rare disease drug discovery approach.

12:05 PM - 12:45 PM

Opportunities and Challenges Using AI in unlocking the ADME Tox Obstacles in Drug Development

This session will address how machine and active learning are being applied to solve complex and difficult ADME Tox problems facing the pharma industry.  There will be representation from both in-house and external models as well as pro and con viewpoints on applying AI to the challenges of the ADME/tox field.  Industry innovators and researchers share their efforts in solving these challenges which could be worth hundreds of millions to industry, accelerate drug discovery and be invaluable to patients.
  • What problems are we trying to solve?
  • Do we have the right data to build ADME/Tox models?
  • How do we know when models will be applicable in a drug discovery program?
  • Is Deep Learning the solution to our problems?
  • How can we staff AI in drug discovery?



12:45 PM - 1:35 PM

Networking Lunch & Expert One-to One Meetings

AI Café

1:35 PM - 1:55 PM

Applying AI to Accelerate Drug Discovery & Design from Patient Data to Drug

A survey of methods that can be used to discover novel targets from patient data, validate those targets from literature mining and drug safety data, design novel leads for those targets and insure that the lead candidate has good drug like properties – all enhanced with a variety of AI methods to be presented.

1:55 PM - 2:25 PM

Applying AI to Augment Regulatory Affairs Knowledge

Technological capabilities in the area of AI are advancing rapidly. Meanwhile, data is being collected from numerous sources, from electronic health records to wearable devices. In addition, the drug development landscape is becoming more transparent as further data is made publicly available. AI can be used to analyze this data and facilitate access to information and insights needed to formulate drug development and regulatory strategies. This presentation will examine opportunities and challenges related to the use of data science and AI to better understand and innovate within the regulatory environment.
  • Henry

    Henry "Skip" Francis, M.D. Director for Data Mining and Informatics Evaluation and Research
    Office of Translational Sciences
    Center for Drug Evaluation and Research (CDER), Food and Drug Administration (FDA)

  • Nicholas Drago

    Nicholas Drago Assistant Director of Regulatory Policy & Intelligence Bayer Pharmaceuticals

2:25 PM - 2:45 PM

The Future of Clinical Trials Using AI

As cost pressures continue to escalate, AI/ML are becoming valuable tools to help sponsors more accurately predict the feasibility of trials in the design phase to mitigate the risk of delays and failure. Using AI in study design, and more effective study protocols, has the potential to reduce cycle time and cut costs to produce relatively faster ROI results. This session will cover how to integrate AI/ML into clinical trial design effectively including how using AI to analyze diverse and extensive data sets has the potential to shape insights from real-world data (RWD) into protocol designs.
  • Nitin Naik

    Nitin Naik Vice President-Global Life Sciences, Transformational Health Frost & Sullivan

2:45 PM - 3:15 PM

Afternoon Refreshments & Expert One-to-One Meetings

AI Café

3:15 PM - 3:50 PM

Patient Selection, Recruitment & Retention

  • Patient selection using AI to filter and improve results and share a roadmap explaining how the technology is being used.
  • How AI is changing the clinical trial enrollment process by transforming the way researchers identify and recruit eligible patients.
  • Using AI and NLP to mine structured and unstructured patient data leads to finding better-matching patients for trials in minutes not months.
  • How AI is improving the process of conducting clinical trials, specifically the impact on patient retention through AI applications in monitoring and diagnostic.
  • Findings on how patients view the use of AI in their trials.



3:50 PM - 4:10 PM

Optimizing Clinical Trials Using AI

AI and related technologies hold the potential to solve many key clinical trial challenges. Change will not come quickly but the potential results are worth taking the first step in the journey. This session will cover what expectations are realistic in optimizing clinical trials in the near term and in the future as AI continues to be adopted across the industry.
  • Current practices and what can be supplemented by technology?
  • Competencies and capabilities needed in the future.
  • Novel models for clinical trials including robotic process automation.
  • Mohammed Ali

    Mohammed Ali Global Head Digital Trials- Global Clinical Operations Boehringer Ingelheim Pharmaceuticals, Inc.

4:10 PM - 4:30 PM

Fireside Chat: Getting Corporate Culture Right to Improve Business Outcome Conversations and Results

Biopharma companies are full of extremely intelligent people but they are generally lacking the specific capabilities needed to maximize the benefits of AI/ML. This discussion session is a great opportunity to share your experiences focusing on these questions and more: How do you tackle the challenge of recruiting and retaining the best technologists if you haven’t updated your technology and IT processes? What is the cultural transformation other industries have undertaken that biopharma can adopt to have business outcome conversations and not just data conversations?  How do you create a flexible culture where communication between technical and non-technical people is valued and effective?

4:30 PM - 5:00 PM

Innovation Spotlight ChangeMaker Presentations

How much can you learn in five minutes? Quite a bit. Hear quick hitting presentations on the latest products, new releases, exciting innovations, and more. Each presenter will speak for 5 minutes and then will be available to answer questions during the reception.

David Wild, Ph.D. — Knowledge Graphs: A Transformative New Approach for Drug Discovery
  • Knowledge Graphs (KGs) are a transformative new way to think about drug discovery data
  • KG-based tools open up powerful new scientific and decision-making capabilities
Paul Courtney — Applications of Machine Learning in Clinical Data Management

Rashan Jibowu — Driving Strategic Decisions with Machine Learning

Leila Pirhaji, Ph.D. — AI-Driven Drug Discovery via Metabolomics
  • Metabolomics, or studying small molecules, are essential for discovering the right therapeutics for the right patients
  • How AI can contribute to characterizing a large number of metabolites, or small molecules, inexpensively and fast
Wayne R. Danter, MD — DeepNEU: Cellular Reprograming Comes of Age–a Machine Learning Platform with Application to Rare Diseases Research
  • Stem cell research will inevitably be transformed by computer technologies. The results of the initial DeepNEU project indicate that currently available stem cell data, computer software and hardware are sufficient to generate basic artificially induced pluripotent stem cells (aiPSC)
  • The application of this computer technology to generate disease specific aiPSCs has the potential to improve 1. disease modeling, 2. rapid prototyping of wet lab experiments, 3. grant application writing and 4. specific biomarker identification in a highly cost-effective manner



5:00 PM - 6:00 PM

Evening Reception

AI Café