08 November 2021
12:00 AM - 11:59 PM CST
The Francis Crick Institute
London, UK
08 November 2021
12:00 AM - 11:59 PM CST
The Francis Crick Institute
London, UK
This year's program will include the following sessions. Additional sessions and speakers will be added as they become final. For a full two-day timeline, visit the Schedule at a Glance.
Speaker: Daniel Ting, M.B.B.S., Ph.D. (Singapore National Eye Centre)
Artificial Intelligence in Health: Sky is the Limit
AI for Automated Discovery and Generative Models
This session will explore AI as a research tool and its role in straddling the edge between fundamental technology and applications to drug discovery in the clinic.
We will also bring together researchers in computer science working on generative machine learning methods with researchers in life sciences and industry in the area of drug design, knowledge discovery and other application areas of generative models. Insight from both sides is required to identify challenges and open questions from an application perspective in order to guide methods development in an impactful direction.
Chair: Fernanda Duarte, Ph.D. (University of Oxford)
Chair: Pascal Friederich, Ph.D (Karlsruhe Institute of Technology)
Speaker: Kerry Gilmore (University of Connecticut)
Designing and Combining Automated Synthesis Platforms
Speaker: Ola Engkvist, Ph.D. (AstraZeneca R&D)
AI for Drug Design: Where Are We Now?
Speaker: Rafael Gomez-Bombarelli, Ph.D. (MIT DMSE)
Representing and Optimizing Small Molecules and Biologics
Speaker: Payel Das, Ph.D. (IBM Research)
Learning to Control AI Models for Accelerating Discovery
Speaker: Xiang-Qun (Sean) Xie, E.M.B.A., Ph.D. (University of Pittsburgh)
Computational Methods and Generative Models for Drug Discovery
The Clinical Validation of AI for Next Generation Medicine
The emergence of AI and digital medicine has led to promising advances along the entire continuum of therapeutic development, spanning from discovery to development to administration. Each of these segments include interventional and diagnostic platforms that, when seamlessly integrated, can optimize and streamline how novel therapies are taken to patients. Examples of these technologies include AI optimization platforms for combination therapy design, diagnostic platforms for patient-drug matching and treatment guidance, treatment monitoring and other methodologies that can collectively redefine how medicines are made.
This session will provide a unique look at field-defining AI-based interventional and diagnostic technologies already being validated and/or deployed in clinical settings. Importantly, this session will feature clinicians and technology developers who have successfully taken their AI-driven innovations into first-in-kind trials. Additional insights that are vital towards broad AI deployment (e.g. regulatory engagement, implementation sciences, healthcare economics, policy and beyond) will be explored.
Chair: Dean Ho, Ph.D. (National University of Singapore)
Speaker: Daniel Ting, M.B.B.S., Ph.D. (Singapore National Eye Centre)
Speaker: Xiao Liu, M.B.ChB., Ph.D. (University Hospitals Birmingham NHS Foundation Trust)
Speaker: Edward Chow, Ph.D. (National University of Singapore)
Machine Learning in Drug Discovery
This session will explore the use of AI for the identification and classification of complex cellular and tissue phenotypes to study the genotypic and environmental determinants of disease progression and response to therapy. We will consider a variety of sample types encountered in the drug discovery pipeline, from cell-based assays to patient samples.
Chair: Kurt Anderson, Ph.D. (The Francis Crick Institute)
Speaker: Elodie Pronier, Ph.D. (Owkin)
ML Analysis of Histological and Genomic Data from Patients
Speaker: Lukas Pelkmans (University of Zurich)
Machine Learning in Drug Discovery
Speaker: Leonidas Bleris, Ph.D. (University of Texas at Dallas)
Cell Morphology-based Machine Learning Models for Human Cell State Classification
Speaker: Chris Bakal (The Institute of Cancer Research)
The Shape of Things to Come. Predictive Models of Cancer Fate Fuelled by Image-'omics
Ethics
With the real and potential applications for AI to improve health and health systems on an exponential rise, so are the complex ethical questions surrounding it. We bring together data scientists and others who are investigating these issues to provide insights into their research.
Chair: Fernanda Duarte, Ph.D. (University of Oxford)
Speaker: Finton Sirockin, Ph.D. (Novartis)
Speaker: David Leslie (Alan Turing Institute)
Responsible Data Science for Health and the Life Sciences in the COVID-19 Era
Speaker: Brent Mittelstadt, Ph.D. (University of Oxford)
Bias Preservation in Machine Learning: The Meaning of Fairness in Medical AI
Speaker: Carlos Maria Galmarini, M.D., Ph.D. (Topazium)
From Hippocrates to Artificial Intelligence: Moving Towards a Collective Intelligence
This discussion will feature a Q&A session with topics submitted from the attendee community. Topics will include, but not be limited to:
This is a valuable opportunity for the vendor, academic and industry communities to join together to explore a number of topics affecting the general AI community.