The path to AI federated learning for drug discovery
While the concept is attracting significant attention, understanding of what federated learning is – and who can benefit from it – varies widely across the life sciences industry. Eight experts joined Scientific Computing World at an exclusive roundtable to discuss their perspective, based on years of operating experience (Image: Shutterstock)
In association with
Artificial Intelligence (AI) has become a cornerstone of modern drug discovery, but as models become more sophisticated, the quality and diversity of the data used to train them has become an equally important competitive advantage. Federated learning is emerging as a powerful solution to this.It is a vision that could fundamentally reshape how drug discovery AI is developed. Instead of isolated organisations building models independently, federated learning offers the possibility of connected ecosystems where knowledge can be shared, improved and expanded while protecting each participant's most valuable asset: their data.
Key insights
Discover:
- How can organisations establish trust between collaborators?
- What governance models are required?
- Which technical standards are emerging?
- And what practical steps can smaller organisations take to become part of federated learning networks?
These questions – and many more – are explored in depth in this white paper, produced in partnership with Revvity Signals, featuring insights from leaders at companies such as LiVeritas Biosciences, Apheris, Eli Lilly and Company, and AstraZeneca. Whether you're working in pharmaceutical R&D, biotechnology, AI development or scientific data management, the discussion offers valuable perspectives on where federated learning is today – and where it is heading next.
