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. The challenge is obvious: pharmaceutical companies, biotechs and research organisations hold vast amounts of valuable data, yet concerns around intellectual property, patient privacy and commercial sensitivity make sharing that data difficult. Federated learning is emerging as a powerful solution to this.
Rather than moving sensitive datasets between organisations, federated learning enables AI models to be trained collaboratively while the underlying data remains securely within each organisation. The result is the opportunity to build more accurate, more robust AI models without compromising data ownership or confidentiality.
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.
Niña Cortina, Co-Founder of LiVeritas Biosciences, explained that her organisation is focused on data generation layer data analysis. "We're a company that can produce the type of data that we could contribute to federated learning, but we're also the builder of computational tools that can either consume or contribute to federated models," she said. Her perspective highlights an important shift: federated learning is no longer simply about pharmaceutical companies sharing information; it is creating opportunities for specialist technology providers, contract research organisations and biotechs to participate in collaborative AI ecosystems.
Robin Roehm, CEO and Co-Founder of Apheris, described how federated data networks are enabling organisations to work together on some of the industry's most challenging problems. From the AI Structural Biology Network, which brings together nine of the world's top pharmaceutical companies, to initiatives exploring antibody developability and ADMET prediction, federated learning is beginning to support collaboration at an unprecedented scale.
Large pharmaceutical companies are also offering these capabilities to smaller innovators. Jonathan Gilbert, Senior Director of Ecosystem Growth and Contributor Partnerships at Eli Lilly and Company, is leading the growth of TuneLab, an initiative built on federated learning. "We're using federated learning to engage with small biotechs for them to use the same models that Lilly uses internally," he says. "They can also improve those models by contributing data in a privacy-preserving way."
Lowering barriers
For many smaller companies, this represents a significant change in perception. Federated learning has often been viewed as something reserved for organisations with enormous computational resources and extensive internal datasets. Increasingly, however, the technology is being designed to lower barriers to participation, allowing organisations with specialised expertise or unique datasets to make meaningful contributions.
Technology providers are focused on turning promising concepts into practical research tools. David Gosalvez, Chief Strategy Officer at Revvity Signals, traces the origins of today's momentum back several years, when Lilly began exploring ways to securely share AI models with collaborators. "There's a real momentum behind many-to-many federated learning networks – not just large pharma, but potentially hundreds of companies contributing, even small ones with unique data contributions," he said.
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.
However, important questions remain: 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 The Path to AI Federated Learning for Drug Discovery, an exclusive Scientific Computing World 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.
Download the free white paper here to discover how federated learning is transforming collaborative AI and accelerating the future of drug discovery.
