AI in Pharma: Discovery West Agenda

Registration, Breakfast and Networking

Chair’s Opening Remarks and Setting the Scene

Rohan Ganesh, Partner, Obvious Ventures

Moving the Needle in Early Drug Discovery

Keynote Presentation: Defining a Target in Drug Discovery 

• An overview of the industry
• Discuss the methods in which AI can be utilized to identify quality, druggable target candidates
• A look at how Moderna have utilized new technologies to produce a diverse pipeline of phase 1-3 drug candidates

Wade Davis, VP, Computational Sciences, Moderna

Keynote Panel Discussion with Open Q&A: What Are the Greatest Challenges and Opportunities of Using AI in Drug Discovery Today? 

The use of AI in drug discovery holds the potential to massively accelerate and improve the drug discovery process, ultimately delivering drugs to patients sooner. This panel will discuss the greatest limitations and promises of leveraging AI in drug discovery, and the importance of cross-industry collaboration in overcoming hurdles and improving success.

• What opportunities does AI have to revolutionize drug discovery and advance our scientific understanding?
• What are some examples of the greatest AI-led outcomes in drug discovery to date?
• What are the main challenges when adopting AI in these processes?
• How are different stakeholders currently using AI in drug discovery and addressing these key challenges?
• How can cross industry collaboration help the ecosystem as a whole?

Wade Davis, VP Computational Sciences, Moderna
Patrick Schwab, Senior Director Artificial Intelligence and Machine Learning, GSK
Jadwiga Bienlowska, Senior Director, Head of Computational Biology Oncology R&D, Pfizer

Morning Refreshments and Networking

Navigating Obstacles in AI-Powered Drug Discovery

Presentation: Utilizing Profile-QSAR Massively-Multitask Machine Learning Models Across the Drug Discovery Process

Eric Martin, Director, Computational Chemistry, Novartis Institutes for BioMedical Research

Panel Discussion with Open Q&A: Overcoming Hurdles Preventing Advancements in AI-driven Drug Discovery

Covering key questions spanning from data to cultural acceptance to gender imbalance, this panel dives straight into the biggest challenges in this industry. Join this session to explore the true impact of our data limitations on AI success, the cultural challenges including hype and trust, and the importance of closing the gender gap in AI and data science, plus steps that all organizations and individuals can take to overcome these hurdles.

• Is data the biggest bottleneck in leveraging AI in the drug discovery process, and are we adequately addressing the “data issue” as an industry? What other measures can be taken to improve this?
• How is the lack of clear data standards and sharing platforms hindering innovation in drug discovery?
• What impact have “hype” AI companies had on the industry, and what other “hype” issues exist? How do we increase cultural confidence in AI methods?
• Why is it so crucial to close the gender gap in AI and data science? How does the underrepresentation of women disadvantage companies and their workers?
• What can be done on an industry, organizational and individual level to overcome the biggest challenges in AI in drug discovery?

Li Li, Executive Director, Head of Research Data Sciences, Gilead Sciences
Dan McKay, Executive Director, Ventus Therapeutics
Camilo Zapata, Senior Director, Global Advanced Analytics and Data Management, AstraZeneca
Afia Afzal, Director, Data Science External Innovation, Janssen

Lunch and Networking

AI Innovation in Drug Design

Presentation: Developing Drug Candidates Tailored to Unconventional Binding Sites – A Deep Dive into Revolution Medicine’s Structure-Based Drug Design Capabilities

John Knox, Executive Director, Head of Structural Chemistry and Discovery Sciences, Revolution Medicines

Presentation: Equivariant Generative Models for Conditional Drug Design

Bradley Parry, Head of Artificial Intelligence, AI Therapeutics

Afternoon Refreshments and Networking

Future Prospects

Presentation: A Data-Driven Quantitative Approach to Target Identification – Accelerating the Next Generation of Immuno-oncology Drug Candidates

Mani Mohindru, CEO, Novasenta

Presentation: The Astrazeneca iLab: The Automated Lab of the Future

• An overview of the work currently being done at The iLab Astrazeneca
• Examine how the Astrazeneca iLab is automating the design-make-test-analyze (DMTA) cycle of drug discovery and how this is optimizing drug design
• A mini case study of a small/large molecule drug designed in iLab and currently in clinical trials.

Alexander Marziale, Director, Discovery Sciences, The Astrazeneca iLab

Panel Discussion with Open Q&A: The Lab of the Future and Fully Automated Drug Discovery – How Far Do We Have to Go?

In this closing session, this panel will round off the day of discussions with a look to the future. Join this expert discussion for a look at what the next five years hold for drug discovery, the key challenges to overcome on the roadmap to success, plus a look to the lab of the future and the possibility of a fully automated drug discovery process. 

• What is the ‘lab of the future’ concept and what impact would it have on drug discovery?
• What do we need to achieve in order to enable the next generation of research?
• What benefits does automation have in drug discovery, and where are the current examples of success?
• How does our resources and current understanding of AI limit our ability to achieve full automation?
•  What can we expect the drug discovery process to look like in 5 years time? Where will the next breakthrough be?

Peter Grandsard, Executive Director, Research, Amgen
Alan Cheng, Senior Director, Merck
Alexander Marziale, Director, Discovery Sciences, The Astrazeneca iLab
Peter Skewes-Cox, Associate Director, Novartis Institutes for BioMedical Research

Chair’s Closing Remarks 

Rohan Ganesh, Partner, Obvious Ventures