Technology Is Crucial In Driving Our Success: A Q&A with Lina Nilsson, PhD, Chief Platform Officer at Recursion

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This week we announced that Lina Nilsson, PhD, has been promoted to the role of Chief Platform Officer - a newly created position responsible for overseeing our Recursion OS drug discovery platform. We sat down with Lina to hear her thoughts on what this role means and the future of Recursion’s platform.

1. What does it mean to be Chief Platform Officer?

It's about recognizing that at Recursion, our 'OS' isn't just a metaphor – it's how we fundamentally drive value in the form of new medicines to patients. As Chief Platform Officer, I'm responsible for the continued evolution and integration of that OS. This means ensuring that all the components – what we call our Industrialized Workflows (the standardized processes for different stages of drug discovery), our Modules (the tools and capabilities we use within those workflows), and our Foundations (the essential infrastructure that supports everything) – work seamlessly together to accelerate drug discovery. It's about building and optimizing the engine that powers everything we do, from generating massive datasets to applying cutting-edge machine learning, to ultimately getting better treatments to patients, faster.

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Lina presenting at a recent Download Day.

2. This is a new role - why is this important for Recursion at this point in the company's journey?

This is a pivotal moment for Recursion. We've made incredible strides in generating the data and building the models to really transform drug discovery. And now, especially following our combination with Exscientia, we have a truly end-to-end platform. We can not only identify potential drug candidates but also design and optimize them with increasing precision.

To fully leverage this expanded capability, we need dedicated leadership focused on the platform itself – on ensuring its efficiency, scalability, and continued innovation. By establishing the Chief Platform Officer role, Recursion is signaling its commitment to being a TechBio leader, recognizing that technology is as crucial as biology and chemistry in driving our success.

3. What are you focused on in this role? What are you most excited about in the future?

My focus is really on three key areas. First, it's about deepening the integration between all of our platform components. We've built a strong foundation of tools and workflows, but there's always room to improve how the pieces are connected, how workflows are optimized, and how we leverage insights across the entire drug discovery process. We have a leadership team that's specifically focused on bridging and connecting the pieces – ensuring alignment between all the different parts of the OS.

Second, it's about pushing the boundaries of what our platform can do. This means continued investment in AI and machine learning, automation, and new ways of generating and using data. For example, we're seeing incredible potential in 'world models' – digital simulations of human biology – that can help us more effectively target our experiments.

And finally, it's about ensuring that everything we do ultimately translates to a real impact for patients. I'm incredibly excited about the potential to not just accelerate drug discovery but also to develop entirely new kinds of treatments, and maybe even prevent diseases before they start.

4. For people who are newer to TechBio who are trying to evaluate and understand different technology platforms driving drug discovery, what should they look at? What's important to understand?

That's a great question. There's a lot of hype in this space, so it's important to look beyond the buzzwords. I'd say focus on a few key things:

  • Data: How is the company generating its data, and is it truly unique and powerful? Are they building datasets that allow for a broad view of biology and chemistry? Is the data clean, structured, and fit for the purpose of AI?
  • Integration: Is the technology seamlessly integrated across the drug discovery pipeline, or is it a collection of siloed, “bolt-on” tools? How well do the AI models connect with the experimental work? The most powerful AI systems are those that are deeply ingrained in the laboratory, creating a cycle of prediction and experimentation that allows the AI to get smarter with each cycle.
  • Impact: This is perhaps the most important question to examine: Is the platform actually translating to more drug candidates, faster clinical trials, and better outcomes for patients? This is how we keep ourselves grounded at Recursion – focusing on what matters most – and we’ve already seen strong signals that our platform is allowing us to move faster and more efficiently across our pipeline.

At Recursion, we believe our platform stands out because of our focus on these very principles. We're not just applying AI to drug discovery; we're building a data-first, end-to-end OS to industrialize the entire process and truly deliver for patients.