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

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

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

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





5:00 - 7:00 Welcome Reception in the Exhibit Hall with Poster Viewing (Sponsorship Opportunity Available)

Wednesday, April 22

7:30 am Registration Open and Morning Coffee

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

Pietro Michelucci, PhD, Director, Human Computation Institute






Additional Panelists to be Announced

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


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 Bhisett, 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 Presentation to be Announced

12:15 Sponsored Presentation (Opportunity Available)

12:30 Session Break

Elsevier-square12:40 Luncheon Presentation I to be Announced



1:10 Luncheon Presentation II to be Announced

1:40 Session Break


1:50 Chairperson’s Remarks

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

Luis 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 Talk Title to be Announced

Mark McCreary, Biomarker Data Management and Curation Lead (DevSci Informatics), Genentech

2:55 Presentation to be Announced

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


4:00 Chairperson’s Remarks

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

Ola 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

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.

5:05 Sponsored Presentation (Opportunity Available)


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

6:45 End of Day

Thursday, April 23

7:30 am Registration Open and Morning Coffee

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,




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




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 Presentation to be Announced

11:10 Talk Title to be Announced

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

11:40 Sponsored Presentation (Opportunity Available)

12:10 pm Session Break

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

Todd 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.

12:50 Luncheon Presentation II (Sponsorship Opportunity Available)

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


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

Bino 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

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

3:00 Progress in Diagnosing Rare Disease Patients Leveraging NLP

Tom Defay, Senior Director, R&D Strategy and Alliances, SPMD, Strategy, Program Management and Data Sciences, Alexion

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

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

4:00 Close of Conference

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