This Royal Society conference will bring together stakeholders from industry and academia to explore advances in machine learning and artificial intelligence for biological research, drug discovery and medicine. Whilst these computational technologies are already transforming biological, clinical and pharmaceutical research, significant obstacles remain, in particular around skills, financing, and accessibility. Concerted industry/academia collaboration will be required to address these challenges.
Following a keynote address from Dr Demis Hassabis CBE FREng FRS (DeepMind), three sessions around biology, chemistry and medicine will include talks on machine learning for target discovery and 'omic technologies, AI-based chemoinformatics, and computational clinical trial design, as well as the classical prediction of protein folding.
The conference will conclude with a panel discussion to address how to reintegrate data from specialised biological subdisciplines, reflect on the barriers faced by researchers when accessing and using machine learning technologies, and consider strategies to drive continued interdisciplinary innovation.
About the conference series
Supported by AstraZeneca, the meeting forms part of the Royal Society’s Transforming our future series in the life sciences. These meetings are unique, high-level events that address the scientific and technical challenges of the next decade. Each conference features cutting-edge science from industry and academia and brings together leading experts from the scientific community, including regulatory, charity and funding bodies.
Attending this event
- This will be a hybrid event
- In-person attendance is invitation only
- Please register to attend online
- For the programme, scroll down and click on the arrows to see the speakers and abstracts for their talks
- The Royal Society has an acceptable use policy for all online events, and we expect our users to abide by these guidelines
- For any queries or accessibility requirements, please email the Industry Team.