AI for Drug Discovery

The use of artificial intelligence in biopharma has redefined how scientists develop new drugs, tackle disease, and more. The AI for Drug Discovery track will discuss problems that biopharma organizations are experiencing in harnessing the power of AI and machine learning technologies to maximize and accelerate drug discovery efforts from early stage to adoption to practical application. Speakers will explore the role of AI in developing new drugs, tackling diseases previously deemed too difficult to take on, the R&D process, chemical synthesis optimization, drug repositioning, making sense of clinical data, predicting clinical trial outcomes, finding correct patients for clinical trials, analyzing real-world evidence, making sense of complex medical data, and avoiding "brittle" results based on too little/biased data. How are you moving beyond correlative analysis towards causal analysis so that these approaches can become generally effective? How do you integrate the challenges that exist in the real-world practice of medicine… and with real patients?

Tuesday, October 6

PLENARY KEYNOTE PROGRAM

10:00 am

Welcome Remarks

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute
Scott Parker, Director of Product Marketing, Marketing, Sinequa
10:15 am

NIH’s Strategic Vision for Data Science

Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health
Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health
11:05 am

LIVE Q&A: Session Wrap-Up Panel Discussion

Panel Moderator:
Ari E Berman, PhD, CEO, BioTeam Inc
11:25 am Lunch Break - View Our Virtual Exhibit Hall
11:55 am Recommended Pre-Conference Workshops*
W1: Data Management for Biologics: Registration and Beyond
W2: A Crash Course in AI: 0-60 in Three
W3: Data Science Driving Better Informed Decisions

*Separate registration required. See workshop page for details.

1:55 pm Refresh Break - View Our Virtual Exhibit Hall
2:15 pm Recommended Pre-Conference Workshops*
W4: Digital Biomarkers and Wearables in Pharma R&D and Clinical Trials
W5: AI-Celerating R&D: Foundational Approaches to How Emerging Technologies Can Create Value
W6: Dealing with Instrument Data at Scale: Challenges and Solutions

*Separate registration required. See workshop page for details.

4:15 pm Close of Day

Wednesday, October 7

TRANSFORMING DRUG DISCOVERY WITH ARTIFICIAL INTELLIGENCE

9:00 am

The Emergence of the AI-Augmented Drug Discoverer

Mark Davies, Senior Vice President, Informatics and Data, Biomedical Informatics, BenevolentAI

Drug discovery is an immensely challenging problem. There are currently more than 9,000 untreated diseases with over 300 million people suffering from rare diseases, for which we are unlikely to develop treatments any time soon. The drug discovery process still costs an average of $2.6 billion per drug. Even then, 30 to 50% of top-selling drugs don't work for the patients which they are prescribed for. We build technology in the service of science, specifically using AI to tackle this huge unmet need and to transform the traditional drug discovery process. In this talk, I will discuss how integration of data is the foundation of which everything else is based, and describe our approach to using AI and human expertise to deliver validated unprecedented targets, and to enhance chemical drug design and precision medicine.

9:20 am

Generative Chemistry and Generative Biology for AI-Powered Drug Discovery

Alex Zhavoronkov, PhD, Founder & CEO, Insilico Medicine

The lecture will focus on the development and application of generative models for creating novel compounds and for generating synthetic biological data with the desired properties.


9:40 am

Talk Title to be Announced

Grace Wenjia You, PhD, Director & Head, Global Portfolio Management, Valuation & Analytics, EMD Serono
10:00 am Coffee Break - View Our Virtual Exhibit Hall
Alexander Ivliev, PhD, Director, Bioinformatics, Clarivate Analytics

Since the time of the Genomic Revolution, networks have been key to understanding biology in health and disease. Application of AI to biological networks is far from trivial. In this talk, we discuss the emerging field of network-based artificial intelligence and how it may transform disease understanding and target ID. In presenting potential challenges and solutions, we will further explore how network approaches may help identify targets and advance our understanding of the microbiome. 

11:30 am LIVE Q&A:

Session Wrap-Up Panel Discussion

Panel Moderator:
Alex Zhavoronkov, PhD, Founder & CEO, Insilico Medicine
Panelists:
Mark Davies, Senior Vice President, Informatics and Data, Biomedical Informatics, BenevolentAI
Alexander Ivliev, PhD, Director, Bioinformatics, Clarivate Analytics
Grace Wenjia You, PhD, Director & Head, Global Portfolio Management, Valuation & Analytics, EMD Serono
11:50 am Lunch Break - View Our Virtual Exhibit Hall
11:55 am Interactive Breakout Discussions

Consider joining a breakout discussion group. These are informal, moderated discussions with brainstorming and interactive problem solving, allowing participants from diverse backgrounds to exchange ideas and experiences and develop future collaborations around a focused topic.

Michael Riener, President, RCH Solutions

Join us for a lively discussion among prominent pharma leaders, and learn:

Why, when & how to implement a public Cloud for your computing needs

Challenges and opportunities when setting and managing stakeholder expectations

Critical keys to success to realize the best outcomes

To learn more about RCH Solutions, visit our Virtual Booth

Joe Donahue, Managing Director, Life Sciences, Accenture

Hosted by Joe Donahue, Managing Director, Life Sciences, Accenture

 

Participants include: 

Andreas Matern, Head of Digital Translational Medicine, Sanofi

John Quackenbush, Professor of Computational Biology and Bioinformatics; Harvard T.H. Chan School of Public Health

Seungtaek Lee, VP, Strategic Partnerships and AI RWE Head of CoE; ConcertAI

Preston Keller, PhD, MBA, President & CCO, PercayAI

Philip Payne, PhD, Becker Professor and Chief Data Scientist, Washington University in St. Louis

 

Jeff Evernham, VP of Customer Solutions, Consulting, Sinequa

Most large scale analysis of clinical trial data only leverages part of the picture, ignoring unstructured data and limiting findability across all the information collected throughout multiple disparate data sources.  This roundtable will discuss leveraging a cognitive platform to combine all data from multiple sources into one unified view using a single entry point to the data.

 

Sasha Paegle, Life Science Business Development, Dell Technologies

Evaluating, optimizing and benchmarking of next generation sequencing (NGS) methods are essential for clinical, commercial and academic NGS pipelines. Optimizations for speed and accuracy often require making trade-offs relative to other constraints. Join this roundtable to discuss benchmarking strategies, trade-offs, and the value of benchmarking genomics tools and applications. 

PLENARY KEYNOTE PROGRAM

Michael Schwartz, Head, Product Marketing, Marketing, Benchling

The life science industry has forged ahead with a new generation of therapeutics. A new R&D paradigm is required to develop scientific platforms, manage data complexity, and orchestrate progress across specialized teams. Digital solutions and data ecosystems are at the heart of this, but require both structure and adaptability to thrive in the modern life science R&D environment.

12:30 pm KEYNOTE PRESENTATION & PANEL DISCUSSION:

Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

Allison Proffitt, Editorial Director, Bio-IT World

While the precision medicine movement augurs for better outcomes through targeted prevention and intervention, those ambitions entail a bold new set of data challenges. Various panomic and traditional data streams must be integrated if we are to develop a comprehensive basis for individualized care. However, deriving actionable information requires complex predictive models that depend on the acquisition and integration of patient data on a massive scale. This picture is further complicated by new data streams emerging from quantified self-tracking and health social networks, both of which are driven by experimentation-feedback loops. Tackling these issues may seem insurmountable, but recent advancements in human/AI partnerships and crowdsourcing science adds a new set of capabilities to our analytic toolkit. This session describes recent work in online collective systems that combine human and machine-based information processing to solve biomedical data problems that have been otherwise intractable, and an information processing ecosystem emerging from this work that could transform the landscape of precision medicine for all stakeholders. Pietro will open with a framing talk, followed by short presentations from each panelist, ending with a moderated Q&A discussion by Allison with speakers and attendees. 

Panelists:
Seth Cooper, PhD, Assistant Professor, Khoury College of Computer Sciences, Northeastern University
Lee Lancashire, PhD, CIO, Cohen Veterans Bioscience
Pietro Michelucci, PhD, Director, Human Computation Institute
Jérôme Waldispühl, PhD, Associate Professor, School of Computer Science, McGill University
1:55 pm Refresh Break - View Our Virtual Exhibit Hall

ACCELERATING DRUG DISCOVERY THROUGH IMAGING AND FLOW CYTOMETRY

2:10 pm

Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging

Ola Spjuth, Professor, Department of Pharmaceutical Biosciences, Uppsala University

We are establishing an automated lab for cell profiling of drugs using multiplexed fluorescence imaging together with label-free quantitative phase imaging. This talk presents our progress and showcases how a small lab can implement a DevOps approach with modern IT solutions to carry out and sustain online prioritization of new data, coupled with continuous AI modeling building on hybrid infrastructure and microservice architecture. A key objective is on improving screening and toxicity assessment using AI-guided intelligent experimental design.

2:30 pm

Accelerating Drug Discovery through the Power of Microscopy Images

Shantanu Singh, PhD, Senior Group Leader, Imaging Platform, Broad Institute

Images contain rich information about the state of cells, tissues, and organisms. We work with biomedical researchers around the world to extract quantitative information from images, particularly in high-content screening experiments involving physiologically relevant model systems. As the biological systems and phenotypes of interest become more complex, so are the computational approaches needed to properly extract the information of interest; we continue to bridge the gap between biologists’ needs and the latest in computational science (e.g., deep learning).

Beyond measuring features biologists specify, we extract more from images through profiling experiments using the Cell Painting assay, where thousands of morphological features are measured from each cell’s image. We are working to harvest similarities in these “profiles” for grouping genes, identifying the functional impact of cancer-associated alleles, discovering disease-associated phenotypes, and identifying novel therapeutics. Ultimately, we aim to make perturbations in cell morphology as computable as genomics data.

All novel algorithms and approaches from our laboratory are released as open-source software, including CellProfiler, CellProfiler Analyst, and cytominer.

2:50 pm Refresh Break - View Our Virtual Exhibit Hall
3:10 pm

Machine Learning Advances Flow Cytometry Analysis – Advancing Programs in Immunosciences and Immuno-Oncology

Luis A. Mendez, Senior Scientist, Drug Discovery, Bristol Myers Squibb Co.

The talk presents advances in multiparameter flow cytometry analysis using machine learning algorithms. Both t-distributed Stochastic Neighbor Embedding (t-SNE) and FlowSOM algorithms are very effective in the comprehensive analysis and visualization of multiparameter flow cytometry data, resulting in a deeper understanding of disease biology at the single-cell level. A cloud-based, high-performance compute environment, coupled with GPU processing, were deployed to overcome challenges with executing these CPU/RAM/GPU-intensive algorithms on large datasets.

4:00 pm LIVE Q&A:

Session Wrap-Up Panel Discussion

Panel Moderator:
Ola Spjuth, Professor, Department of Pharmaceutical Biosciences, Uppsala University
Panelists:
Shantanu Singh, PhD, Senior Group Leader, Imaging Platform, Broad Institute
Luis A. Mendez, Senior Scientist, Drug Discovery, Bristol Myers Squibb Co.
4:20 pm Bio-IT Connects - View Our Virtual Exhibit Hall
5:00 pm Close of Day

Thursday, October 8

USING AI TO PROPEL THE DRUG DISCOVERY AND DEVELOPMENT PIPELINE

9:00 am

Real-World Data and AI Hype: Stimulating and Supporting Each Other

Dorothee Bartels, PhD, Global Head of RWE, UCB

The real world data hype caused high expectations, including RCTs might only play a minor role in future drug development. But  they are rather complementary to RCT data and cannot replace them. Artificial intelligence may change  drug development and time to market significantly, but will not replace past knowledge and experience. Real World Evidence generation can be enhanced by AI and is key for public health.

9:20 am

New Methods to Integrate and Leverage Genomic and Clinical Data to Improve Rare Disease Diagnostics

Tom Defay, Deputy Head, Diagnostics, Alexion Pharmaceuticals

Rare disease patients suffer too often from long diagnostic delays and misidentified diseases. This creates a significant burden, not just for patients, but for healthcare systems. We present in this talk examples of instances where we have collaborated with researchers and hospital systems to develop novel approaches for rare disease patient identification using tools like genomics, machine learning, and NLP.

9:40 am Coffee Break - View Our Virtual Exhibit Hall
10:00 am

Machine-Learned Molecular Models for Protein Structure, Networks, and Design

Mohammed AlQuraishi, PhD, Assistant Professor, Systems Biology, Columbia University
10:20 am

ChemDataExplorer: A Data Warehouse, Visualization and Machine Learning Platform for Small Molecules

Peter V. Henstock, PhD, Machine Learning & AI Lead, Software Engineering & Statistics & Visualization, Pfizer Inc.

 

This presentation describes a novel data warehouse solution that captures and integrates 8 major public datasets of small molecule data.  With a pharmacology focus, the data are available for search, visualization of compound space, and machine learning.  


Jelena Dowey, Gabriel Sanna, Max Shirvanifar, Sishir Yeety, Lu Wang, Nicolas Tejera, Nandini Patel, Ravi Shanker, Peter V. Henstock
Harvard Extension School and Pfizer Inc.
Philip R.O. Payne, PhD, FACMI, FAMIA, Becker Professor and Chief Data Scientist, Washington University in St. Louis, School of Medicine

As an alternative to traditional drug discovery approaches, in silico hypothesis generation has potential to accelerate timely, cost-efficient identification of new uses for existing therapies. We’ll explore the current-state-of-the-art for mining extant data and knowledge resources, artificial intelligence methods, in order to generate and validate hypothesis concerning drug repositioning candidates.

11:10 am LIVE Q&A:

Session Wrap-Up Panel Discussion

Panel Moderator:
Peter V. Henstock, PhD, Machine Learning & AI Lead, Software Engineering & Statistics & Visualization, Pfizer Inc.
Panelists:
Dorothee Bartels, PhD, Global Head of RWE, UCB
Tom Defay, Deputy Head, Diagnostics, Alexion Pharmaceuticals
Mohammed AlQuraishi, PhD, Assistant Professor, Systems Biology, Columbia University
Philip R.O. Payne, PhD, FACMI, FAMIA, Becker Professor and Chief Data Scientist, Washington University in St. Louis, School of Medicine
Jane Reed, Director, Life Science, Linguamatics, an IQVIA Company
Raveen Sharma, Managing Director, Life Sciences and Healthcare, Deloitte Consulting LLP
Lu Wang, Founder and CEO, Komodotech
11:30 am Lunch Break - View Our Virtual Exhibit Hall
11:35 am Interactive Breakout Discussions

Consider joining a breakout discussion group. These are informal, moderated discussions with brainstorming and interactive problem solving, allowing participants from diverse backgrounds to exchange ideas and experiences and develop future collaborations around a focused topic.

Timothy Gardner, CEO, Riffyn, Inc.

How do you use data / digitization today to drive scientific discovery / product development?

What are you greatest scientific pain points / gaps that are not being met by digitization?

What kinds of outcomes do you believe digital tools could help you achieve?

 

Scott Jeschonek, Principal Program Manager, Microsoft Azure

Welcome to this discussion group on the growth of demand for HPC in scientific research. We are looking forward to a lively forum. We'll start by looking at three related topics:

- What events trigger demand in your organization? How has the current pandemic impacted resources?

- What could make scale and collaboration more accessible to more researchers?

- Share a recent experience of shifting workloads to manage HPC capacity.

Greg DiFraia, General Manager, Americas, Executive Team, Scality
Shailesh Manjrekar, Head of AI and Strategic Alliances, Executive Team, WekaIO

In this session we’ll discuss how to provide researchers with performance and scale in genomics & research analytics, to drive results at a price point that’s economically viable on public & private cloud.

11:35 am

Breakout: NGS Pipeline Optimizations

Tristan J Lubinski, PhD, Sr Scientist, Next Generation Sequencing Informatics, AstraZeneca Pharmaceuticals; Co-organizer, Boston Computational Biology and Bioinformatics (BCBB)
Howard Marks, Technologist Extraordinary and Plenipotentiary, VAST Data

Storage solutions we’ve been using force bioinformaticists to make trade-offs between the capacity and low-cost of disk and the performance of flash. This results in complex tiering configurations that only deliver performance for a small slice of the data. In this session, we will review how advancements in technology enable VAST Data to revolutionize the cost of all-flash and allows bioinformatists faster analysis across larger datasets for deeper insights.

PLENARY KEYNOTE PROGRAM

12:00 pm

Welcome Remarks

Cindy Crowninshield, Executive Event Director, Cambridge Healthtech Institute
Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP
12:15 pm

Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert C. Green, Professor & Director, G2P Research, Genetics & Medicine, Brigham & Womens Hospital
12:40 pm

AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Z. Jovanovic, PhD, Chief Digital Officer, Sanofi
1:05 pm

LIVE Q&A: Session Wrap-Up Panel Discussion

Panel Moderator:
Vivien R. Bonazzi, PhD, Managing Director & Chief Biomedical Data Scientist, Deloitte Consulting LLP
Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP
1:25 pm Happy Hour - View Our Virtual Exhibit Hall
2:00 pm Close of Conference





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