Track 6: AI for Drug Discovery and Development

Harness the Power of AI and Machine Learning to Maximize and Accelerate Drug Discovery and Developme

May 4 - 5, 2022 ALL TIMES EDT

The AI for Drug Discovery and Development track will discuss opportunities and challenges that biopharma organizations are experiencing in harnessing the power of artificial intelligence and machine learning technologies to maximize and accelerate drug discovery and development efforts from early stage to adoption to practical application. Speakers will explore the role of AI in transforming disease understanding and target ID, approaches using AI and human expertise to help identify and deliver validated targets, as well as enhance chemical drug design and precision medicine.

Tuesday, May 3

7:00 am Registration Open (Plaza Level Lobby)
8:00 am Recommended Pre-Conference Workshops and Symposium*

On Tuesday, May 3, 2022 Cambridge Healthtech Institute is pleased to offer nine pre-conference workshops scheduled across three time slots (8:00-10:00 am, 10:30 am-12:30 pm, and 1:45-3:45 pm) and a Symposium from 8:25 am-3:45 pm. All are designed to be instructional, interactive and provide in-depth information on a specific topic. They allow for one-on-one interaction and provide a great way to explain more technical aspects that would otherwise not be covered during the main conference tracks that take place Wednesday-Thursday.

*Separate registration required. See Workshop page and Symposium page for details.

3:45 pm Session Break and Transition to Plenary Keynote

PLENARY KEYNOTE LOCATION: 210 (Overflow 208)

PLENARY KEYNOTE PROGRAM

4:00 pm

Welcome by Conference Organizer

Allison Proffitt, Editorial Director, Bio-IT World
4:05 pm Innovative Practices Award
Mike Tarselli, PhD, Chief Scientific Officer, TetraScience
4:30 pm

Ask What IT Can Do for Bio...and What Bio Can Do for IT

George M. Church, PhD, Robert Winthrop Professor, Genetics, Harvard Medical School

IT for Bio: In May 2021, one haploid human genome (3.055 billion bp) was sequenced completely, but zero diploid. We have 7.7 billion diploid humans yet to be sequenced and correlated with their environments and traits in the Personal Genome Project. Plus, at least one genome from each of over 8.7 million eukaryotic species in the Earth Biogenome project. Plus, monitoring pathogenic and commensal bacteria, allergens, and viruses in the BioWeatherMap. Plus, ancient DNA. We are counting RNA molecules per cell in most (or all) cell types in humans, mice, and many other species throughout development and connectome (with imaging resolution up to 20 nm).   

Bio for IT: Reading and writing DNA has improved exponentially in cost (at least 60 million fold) and is increasingly used for storing non-biological data. The record for editing DNA in vivo is now 24,000 edits per cell and for storing data in vivo is about 1 terabyte per mouse. Enormous chemical and biological 'libraries' can perform 'Natural Computing' for tasks far beyond current von-Neumann silicon and quantum computers. The combination of these – machine learning + megalibraries (ML-ML) is already having commercial impact (e.g. Nabla, Manifold, Dyno, Patch). 

5:45 pm Welcome Reception in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)
7:00 pm Close of Day

Wednesday, May 4

7:00 am Registration Open and Morning Coffee (Plaza Level Lobby)

PLENARY KEYNOTE ROOM LOCATION: 210

PLENARY KEYNOTE PROGRAM

8:00 am

Welcome by Conference Organizer

Allison Proffitt, Editorial Director, Bio-IT World
Zachary Powers, Chief Information Security Officer, Benchling
8:15 am

Accessing and Securing the Data that Drives Breakthroughs

Allison Proffitt, Editorial Director, Bio-IT World
Rachana Ananthakrishnan, Executive Director, Globus, University of Chicago
Ari E. Berman, PhD, CEO, BioTeam, Inc.
Jonathan C. Silverstein, Chief Research Informatics Officer & Professor, Biomedical Informatics, University of Pittsburgh
Rebecca F. Rosen, PhD, Director, Office of Data Science and Sharing, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health

Life sciences research is generating massive amounts of data that should be accessible to collaborators and colleagues to enable breakthrough discoveries. However, ensuring sensitive data are shared securely in a manner that protects patient privacy and complies with myriad regulations is a daunting task, which often slows the pace of research. Our panel of leading practitioners will share insights on the challenges and best practices of managing protected research data.

9:30 am Coffee Break in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)

ROOM LOCATION: 206

AI AND MACHINE LEARNING FOR TARGET DISCOVERY AND NAVIGATING CHEMICAL SPACE

10:15 am Organizer's Remarks
10:20 am

Chairperson's Remarks

Rishi R. Gupta, PhD, Associate Director, Data Science, Novartis Institute for Biomedical Research
10:25 am

Leveraging AI to Navigate Chemical Space and Drug Challenge Targets

Huijun Wang, PhD, Director Computational Drug Design, Cheminformatics & AI in Chemistry, Agios Pharmaceuticals

This talk will focus on how to leverage AI to navigate large chemical spaces to support hit finding and lead optimization for challenge targets. It will describe how to use AI approaches to improve the standard library design, virtual screening, and hits expansion to support lead finding. It will also cover some concepts to leverage AI to rapidly generate testable hypothesis for lead optimization.

10:55 am

AI and Machine Learning for Target Discovery

Xiong Sean Liu, PhD, Director, Data Science & Artificial Intelligence, Novartis

Target identification and validation is a critical step in the drug discovery pipeline. AI and machine learning (ML) techniques are increasingly being used for target discovery. This talk will provide an overview of AI/ML techniques in this space, including natural language processing, Bayesian networks, classical machine learning, and more recent deep learning methods such as graph neural networks and embeddings. We also discuss current research gaps and future opportunities.

Erin Davis, PhD, Senior Vice President of Enterprise Informatics, Schrödinger

We are entering a new phase of computationally-guided, informatics-intensive, collaborative drug discovery. We outline a digital chemistry strategy enabled by efficient exploration and scoring of vast, intelligently enumerated chemical space through the amplification of physics-based principles with machine learning. We describe the ‘Lab of the Future’ based on highly predictive models utilized across project teams. Case studies from drug discovery programs in preclinical and clinical stages.

Sanjay Saraf, Head of Data and Analytics Product Management, Benchling

As much as we hear about ML, not many are taking advantage of of it in their scientific work. By delivering ML-driven functionality for targeted use cases, modern software can turn the theoretical into practical tools. This presentation focuses on two use cases: protein structure prediction and strain parameter optimization. We’ll highlight how Benchling makes these tasks more efficient, and highlight the impact of customers currently taking advantage of ML as part of this everyday work.

VK Gadi, Director of Medical Oncology, UI Health & Associate Director, University of Illinois Cancer Center
Rachel Yung, MD, Associate Professor, Medical Oncology, University of Washington & Physician, Seattle Cancer Care Alliance
Jingqing Zhang, Head of AI, Pangaea Data

Understanding the impact of precision medicine on medical practice, patient care and clinical outcomes is essential for advancing cancer care. However, extracting tumor genomic testing (TGT) from EHRs is challenging. This presentation will review a pilot study, conducted between leading US-based clinicians and Pangaea, to assess the ability for Natural Language Processing (NLP) algorithms to convert unstructured text data and PDF-formatted TGT results into research quality data. Results showed that Pangaea’s AI-driven product, PIES, was proven to extract 26 variables (for demographics, genomic testing results and social indicators), with an average accuracy of 97.3% (100% for 14 variables), to create research quality data.

12:55 pm Session Break and Transition to Luncheon Presentation
Aurora Costache, Customer Engagement Manager, Elsevier Professional Services Group, Elsevier

Learn how to create impactful machine learning models and ensure early adoption into your daily workflow. We will share our experience working on a Blood-Brain-Barrier penetration machine-learning model using data from Reaxys.

Find out how to optimize model development through a cross-functional/organizational team approach and how Elsevier can supplement your efforts with data, support and services to enable an end-to-end development and deployment of machine-learning models.

1:50 pm Refreshment Break in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)
2:35 pm

Chairperson's Remarks

Kevin Davies, PhD, Executive Editor, The CRISPR Journal; Author, Editing Humanity: The CRISPR Revolution and the New Era of Genome Editing
2:40 pm

Using AI for Immunogenicity Potential Assessment in Drug Discovery

Jiayi Cox, PhD, Data Scientist II, Novartis Institutes for BioMedical Research (NIBR)

Anti-drug antibodies developed against therapeutic proteins have been shown to reduce the medication efficacy, and in worst-case this immunogenicity can have safety implications for the patient. Understanding and predicting the immunogenic potential of therapeutic proteins has been a major challenge in the process of biotherapeutic drug discovery. In recent years, many AI methods have been applied to protein fragments to evaluate their immunogenicity potential and have shown encouraging accuracies. This presentation will provide an overview of popular AI techniques in this space, including natural language processing, position-specific scoring matrix, deep motif deconvolution, etc. Current use cases of such tools, research gaps, and future opportunities will also be discussed. 

3:10 pm

DIP- Enabling DL Analysis by Automating Image Processing & Data Enrichment

Radha Konduri, Manager, Digital Capability Management, Bristol Myers Squibb Co.

Advanced digital microscopes have revolutionized biology & pharmaceutical research, generating massive volumes of data. But scientists can’t study what they can’t measure. The need to quantify biological characteristics or to calculate metrics has driven a need to seamlessly ingest, annotate, share, analyze, & archive datasets. Discovery Imaging Platform makes image datasets easily discoverable, accessible, transformable, analyzable, & ensures that derived numeric datasets are easily transferable to DL data analysis platforms.

3:40 pm

Artificial Intelligence Accelerates Drug Discovery – Status & Opportunities

Unmesh Lal, Director, Healthcare & Life Sciences, Frost & Sullivan, Inc.

Artificial Intelligence (AI) for drug discovery and development has entered a growth phase. This presentation will focus around the commercial aspects and call out major current and emerging application areas of AI/ML within the sector. AI-enabled competencies across big tech and start-ups will be covered followed by a brief assessment of the therapeutic area-wise activity and recent trends. Preview of the emerging ecosystem, partnerships, and selective case studies will also be discussed.

Chris McCready, Principal Research Scientist, Sartorius Corporate Research
Yev Monisova, Manager, Life Sciences Practice, Kanda Software

Cell Insights is a virtual bioreactor that runs in silico experiments to simulate cell culture growth and identifies cells suited for intensified or continuous processing.  The application addresses the challenges of commercial antibody production such as low and inconsistent yields, high costs of labor and reagents, and extensive timelines for experiment setup and optimization. It incorporates traditional mathematical models and novel ML/AI algorithms to provide accurate and informative experimental simulations.

4:40 pm Best of Show Awards Reception in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)
6:00 pm Close of Day

Thursday, May 5

7:30 am Registration Open and Morning Coffee (Plaza Level Lobby)

PLENARY KEYNOTE ROOM LOCATION: 210

PLENARY KEYNOTE PROGRAM

8:00 am Welcome by Conference Organizer
8:00 am

Welcome by Conference Organizer

Allison Proffitt, Editorial Director, Bio-IT World
Nate Raine, Director Data Custodians, Lifebit
8:15 am

Leveraging Large-Scale Human Data to Advance and Accelerate Drug Discovery

Shankar Subramaniam, PhD, Distinguished Professor of Bioengineering; Professor of Chemistry, Biochemistry and Nanotechnology; Adjunct Professor of Cellular & Molecular Medicine, University of California at San Diego

Advances in genomics technologies have led to generation of massive amounts of human data. This has catalyzed new insights into cellular processes in the normal and disease state and facilitated the search for safe and effective medicines. The UK Biobank, All of US and TopMed initiatives are exemplars of this approach. We highlight examples from our lab where meaningful insights have been obtained advancing our understanding of disease biology and its pharmacological application.

9:30 am Coffee Break in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)

ROOM LOCATION: 206

USING AI TO TRANSFORM DISEASE UNDERSTANDING, TARGET ID, AND PRECISION MEDICINE APPROACHES

10:15 am Organizer's Remarks
10:20 am

Chairperson's Remarks

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
10:25 am

Precision Medicine…First We Need Accurate Medicine

Michael Liebman, PhD, Managing Director, IPQ Analytics, LLC
Michael Montgomery, MD, Co-Founder and CEO, Stable Solutions LLC
Jonathan Morris, MD, Vice President, Provider Solutions; Chief Medical Informatics Officer, Real World Insights, IQVIA

Precision Medicine has evolved with a focus on using new technologies, e.g. NGS, Big Data Analytics, ML/AI/Deep Learning to better stratify patient populations. The results of these studies directly impact patient management, development of new therapeutics and diagnostics and even payer reimbursement policies but may not adequately reflect on the complexity of clinical medicine in which accuracy in diagnosis is critical. Diseases are processes, not states; many “diseases” are really syndromes; patients have co-morbidities and are impacted by lifestyle and environment; differential diagnosis is frequently not the result of “rule in” of disease specific clinical presentation, but rather “rule out” potentially similar conditions. There is a greater need for transparency about the accuracy of a diagnosis before we can apply precision medicine approaches. Examples will include the challenges in diagnosing and managing multiple sclerosis.

11:55 am

Accelerating Therapeutics for Opportunities in Medicine (ATOM): New Horizons for Integrated Modeling with AI Drug Discovery

Eric Stahlberg, PhD, Director, Cancer Data Science Initiatives, Frederick National Laboratory for Cancer Research
12:25 pm

A Novel AI Platform to Understand Diseases with Previously Undiscovered Mechanisms for Purposeful Drug Creation

Fabrice Chouraqui, PharmD, CEO, Cellarity

Cellarity uniquely combines high-resolution data, single cell technologies, and machine learning to encode biology, simulate interventions, and purposefully design breakthrough medicines. By focusing on the cellular changes that underlie disease instead of a single target, Cellarity’s approach uncovers new biology and treatments and is applicable to a vast array of disease areas.

12:55 pm Session Break and Transition to Luncheon Presentation
Kas Subramanian, PhD, Executive Director, Modeling, Applied BioMath

The traditional process of drug discovery is complex, requiring the laborious experimental assessment of thousands of targets, hits, leads and candidates, making it expensive and time-consuming. Machine learning (ML) approaches provide methods that can improve the efficiency of the discovery process by formally integrating insights from data generated both in the public domain as well as internally. This talk will focus on case studies that demonstrate the application of ML to target validation and lead optimization and illustrate how machine learning methods can be used for decision making with quick informed predictions that can be rapidly validated by targeted experiments. 

1:50 pm Refreshment Break in the Exhibit Hall with Poster Viewing (Auditorium/Hall C)

ROOM CHANGE: 209

DATA-DRIVEN BIOPHARMA R&D – ACCELERATING INNOVATION THROUGH AI/ML

2:35 pm

Chairperson's Remarks

Nimita Limaye, PhD, Research Vice President, Life Sciences R&D Strategy and Technology, IDC
Sidd Bhattacharya, Director, Cloud & Digital Transformation, PwC
Matt W. Maddox, Associate Vice President, Eli Lilly & Company
Kosmas Kretsos, PhD, MBA, Global Business Development Lead, Healthcare and Life Sciences, Amazon Web Services (AWS)

Outside of healthcare, most industries are using large datasets and AI/ML to optimize daily business operations. But, in the pharmaceutical industry, once a clinical trial is completed and submitted, the data is rarely seen or utilized again. Join PwC to learn how the pharmaceutical companies of tomorrow are bringing together AI-enabled analytics and automation to design clinical trials that bring lifesaving treatments to patients faster.

3:10 pm

Operationalization of Predictive Models for Large Molecules in Research at Sanofi

Yves Fomekong Nanfack, Director, Head of Operations Digital Biologics Platform, Sanofi

In a large organization such as Sanofi, to better leverage predictive models developed by data scientists it is essential to have an operation model that allows us to move from “experiments” to impactful models leveraged by any. Sanofi is investing in developing key approaches that enable global operationalization of our predictive models with the goal of accelerating the discovery of large molecules.


3:40 pm

How Digital Disruption is Hyperscaling Innovation in Biopharma R&D

Nimita Limaye, PhD, Research Vice President, Life Sciences R&D Strategy and Technology, IDC

The speed at which vaccines were launched in the past year has raised the bar, demanding accelerated innovation. The biopharma industry has leveraged innovative technologies ranging from the use of GPU-powered transformer models, federated learning platforms, and data fabrics, and has disrupted conventional models fueling co-innovation. AI is being used to design precision-engineered drugs and innovative strategies involving the use of computer vision are being leveraged to identify lead candidates for multiple diseases. Specific process analytical technologies (PAT) and intelligent supply chains are being developed for the manufacturing of highly complex and time and cost-intensive cell and gene therapies. While cloud-native platforms and high-performance computing are hyperscaling innovation, there are still significant concerns around data security and data ownership.

4:10 pm Close of Conference





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