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?

Final Agenda

 

Monday, April 20

9:00 am - 5:00 pm Hackathon*

*Pre-registration required.

Tuesday, April 21

7:30 am Workshop Registration Open and Morning Coffee

8:30 am - 3:30 pm Hackathon*

*Pre-registration required.


8:30 - 11:30 am Recommended Morning Pre-Conference Workshops*

W2. A Crash Course in AI: 0-60 in Three

Gustavo Arango, PhD, Senior Data Scientist - Oncology Bioinformatics, AstraZeneca

Bino John, PhD, Associate Director, Data Science - Clinical Pharmacology & Safety Sciences, AstraZeneca R&D

John Van Hemert, PhD, Research Scientist, Bioinformatics, Corteva Agri Science, A Dow-Dupont Division

12:30 - 3:30 pm Recommended Afternoon Pre-Conference Workshops*

W14. Deep Learning for Image Analysis

Peter Henstock, PhD, AI & Machine Learning Lead, Pfizer

*Separate registration required.

2:00 - 6:30 Main Conference Registration Open

PLENARY KEYNOTE SESSION

4:00 Welcome Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

 

 

 

4:05 Keynote Introduction

4:15 PLENARY KEYNOTE PRESENTATION: 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

 

 

 

 

Riffyn_new 5:00 - 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing

 

 

Wednesday, April 22

7:30 am Registration Open and Morning Coffee

PLENARY KEYNOTE SESSION

8:00 Welcome Remarks

Allison Proffitt, Editorial Director, Bio-IT World

 

 

 

8:05 Keynote Introduction

8:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert Green, MD, MPH, Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School

 

 

 

8:45 PANEL DISCUSSION: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

Seth CooperSeth Cooper, PhD, Assistant Professor, Khoury College of Computer Sciences, Northeastern University

 

 

 

 

 

Lancashire_LeeLee Lancashire, PhD, Chief Information Officer, Cohen Veterans Bioscience

 

 

 

 

 

Pietro Michelucci, PhD, Director, Human Computation Institute

 

 

 

 

 

Jérôme WaldispühlJérôme Waldispühl, PhD, Associate Professor, School of Computer Science, McGill University

 

 

 

 

 

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 talk 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.

9:45 Coffee Break in the Exhibit Hall with Poster Viewing

TRANSFORMING DRUG DISCOVERY WITH ARTIFICIAL INTELLIGENCE

10:50 Organizer’s Welcome Remarks

Cambridge Healthtech Institute

10:55 Chairperson’s Remarks

11:00 Generative Chemistry and Generative Biology for AI-Powered Drug Discovery

Alex Zhavoronkov, PhD, Founder and CEO, Insilico Medicine Hong Kong

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.

11:30 A Quantum-Inspired Method for Three-Dimensional Ligand-Based Virtual Screening

Govinda Bhisetti, PhD, Principal Investigator and Head of Computational Chemistry, Biogen

We describe a quantum-inspired graph-based molecular similarity (GMS) method for ligand-based Virtual Screening (VS). The method is formulated as a quadratic unconstrained binary optimization problem that can be solved using a quantum annealer. We included various features relevant to ligand-based VS, such as pharmacophore features and three-dimensional atomic coordinates. We evaluated this approach on several datasets and found it to yield higher early enrichment values compared to conventional fingerprint approaches.

12:00 pm From Chaos to Clean: Preparing a Data Set for Advanced Analytics

Iarrobino_MichaelMichael Iarrobino, Director, Product Management, Copyright Clearance Center

Raw “un-FAIR” data impedes the ability to leverage AI and ML to advance R&D. In this session, hear how a large corpus of full-text scientific literature was processed for enhanced discovery using NER and other techniques. Learn best practices to prepare data for analytics projects and understand “FAIR”-ified data’s value.

12:15 Sponsored Presentation (Opportunity Available)

12:30 Session Break

Elsevier-square 12:40 LUNCHEON PRESENTATION I: Reaxys-PAI Predictive Retrosynthesis: Rewiring Chemistry and Redesigning Synthetic Routes

Ivan Krstic, PhD, Director, Chemistry Solutions, Elsevier

Increasing the success rate in synthetic chemistry would have a huge benefit in terms of speed, efficiency and cost reduction on drug-discovery projects. Reaxys-PAI Predictive Retrosynthesis solution developed in collaboration between Elsevier and Waller and Segler et al. deploys next-generation technologies to augment chemical synthesis and chemist knowledge helping drive innovation, time and cost savings. This talk will discuss the solution and with feedback from chemists demonstrating its utility with examples from recent drug-development programs.

Clarivate-Analytics 1:10 Luncheon Presentation II to be Announced

1:40 Session Break

ANALYZING ENTIRE MULTIPARAMETER FLOW CYTOMETRY DATASETS

1:50 Chairperson’s Remarks

1:55 Machine Learning Advances Flow Cytometry Analysis – Advancing Programs in Immunosciences and Immuno-Oncology

Mendez_LuisLuis A. Mendez, Senior Research Scientist, Immunoscience Drug Discovery Group, Bristol-Myers Squibb

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 the challenges with executing these CPU/RAM/GPU-intensive algorithms on large datasets.

2:25 Advanced Flow Cytometry Analytics with Novel Data Management and Machine Learning

Bing-Yuan Chen, PhD, Lead Informatics Analyst, Genentech

Flow cytometry scientists spend significant amounts of time performing manual data management, QC, gating, and analysis tasks. These processes are time-consuming, costly, require difficult-to-perform quality control, and result in a high amount of variance and subjectivity. Additionally, this is valuable time that most flow cytometry scientists would rather spend on their research and development endeavors. To address these challenges, we have developed a novel methodology for flow cytometry that focuses on the full data lifecycle and utilizes a machine learning algorithm for automated gating and QC. Together, this end-to-end approach saves time and costs, improves data objectivity and quality, and provides F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data throughout the entire process, freeing up flow cytometry scientists to focus on the science rather than data management.

2:55 Presentation to be Announced

3:25 Refreshment Break in the Exhibit Hall with Poster Viewing

ACCELERATING DRUG DISCOVERY THROUGH AI-GUIDED INTELLIGENT EXPERIMENTAL DESIGN

4:00 Chairperson’s Remarks

4:05 Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging

Spjuth_OlaOla Spjuth, PhD, Associate 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.

4:35 Accelerating Drug Discovery through the Power of Microscopy Images

Singh_ShantanuShantanu 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.

5:05 Sponsored Presentation (Opportunity Available)


Stellus_Technologies

5:35 Best of Show Awards Reception in the Exhibit Hall with Poster Viewing

RedRiver

 

 


6:45 End of Day

Thursday, April 23

7:30 am Registration Open and Morning Coffee

PLENARY KEYNOTE SESSION & AWARDS PROGRAM

8:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

 

 

 

 

8:05 Awards Program Introduction

8:10 Benjamin Franklin Award and Laureate Presentation

J.W. Bizzaro, Managing Director, Bioinformatics.org

 

 

 

 

Discngine8:35 Bio-IT World Innovative Practices Awards

Allison Proffitt, Editorial Director, Bio-IT World

 

 

 

 

9:00 AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur

 

 

 

 

Penguin_Computing_Tagline 9:45 Coffee Break in the Exhibit Hall and Poster Competition Winners Announced at 10:00

 

 

10:30 Organizer’s Remarks

Cambridge Healthtech Institute

10:35 Chairperson’s Remarks

10:40 The Emergence of the AI Augmented Drug Discoverer

Davies_MarkMark Davies, SVP 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 in 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 Mark 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.

11:10 Talk Title to be Announced

You_GraceGrace You, PhD, Director, Head, Global Portfolio Management, Valuation & Analytics, EMD Serono


accenture 11:40 Presentation to be Announced

12:10 pm Session Break

12:20 LUNCHEON PRESENTATION I: AI and the Cloud – Novel Ways to Accelerate Innovation

Neuville_ToddTodd Neuville, Worldwide Lead - Life Sciences, Amazon Web Services

Learn how pharma companies are working with AWS artificial intelligence and machine learning (AI/ML) to accelerate research, enhance their clinical trials, improve manufacturing, and better understand real-world data. Hear how cloud technology is helping to expand the use of AI along the life sciences value chain to accelerate time to market for new products and increase operational efficiency.

CAS_New 12:50 Luncheon Presentation II to be Announced

 

1:20 Dessert Refreshment Break in the Exhibit Hall with Last Chance Poster Viewing

USING AI TO PROPEL THE DRUG DISCOVERY AND DEVELOPMENT PIPELINE

1:55 Chairperson’s Remarks

Bino John, PhD, Associate Director, Data Science – Clinical Pharmacology & Safety Sciences, AstraZeneca R&D


2:00 KEYNOTE PRESENTATION: Accelerated Drug Development Using AI

John_BinoBino John, PhD, Associate Director, Data Science – Clinical Pharmacology & Safety Sciences, AstraZeneca R&D

Drug development is an expensive and costly endeavor, costing on an average of 2.6 billion dollars to bring a drug to market. Data science and artificial intelligence are essential in reducing the costs and time to bring these to the clinic. This talk will highlight some of the current initiatives in analytics at AstraZeneca, spanning chemical and biological data. The talk will provide specific use cases in which we use AI to improve drug design and develop safer medicines.

2:30 Machine-Learned Molecular Models for Protein Structure, Networks, and Design

AlQuraishi_MohammedMohammed AlQuraishi, PhD, Systems Biology Fellow, Harvard Medical School


3:00 New Methods to Integrate and Leverage Genomic and Clinical Data to Improve Rare Disease Diagnostics

delAngel_GuillermoGuillermo del Angel, PhD, Senior Director, Data Sciences, Genomics and Bioinformatics, 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.   

3:30 Mining Drug-Target-Disease Trends from Public Data Sources

Henstock_PeterPeter Henstock, PhD, AI & Machine Learning Technical Lead, Pfizer


4:00 Close of Conference



Platinum Sponsors

accenture

BenchlingNEW

Elsevier-square

L7-informatics

linguamatics

Nutanix

PerkinElmer

Weka