Ron Alfa (NOETIK)
Responses to interview questions from Tal Behar, Precision Medicine World Conference
1. What unique role does Noetik’s “virtual patient” platform play in reshaping how we approach clinical trials or drug development?
The fundamental failure mode in our industry isn’t that we can’t design good molecules; it’s that we test them in models that don’t look anything like the human disease. We’ve spent decades relying on reductionist cell lines that lack context. Noetik is flipping that paradigm by building virtual cells and patients directly from high quality, multimodal human tissue data.
Our platform, powered by OCTO, doesn’t just look at a cell in isolation. It understands spatial context, tissue architecture, the immune microenvironment, and how cells communicate with one another. Our world models allow us to simulate how a virtual patient’s tissue responds to a perturbation in silico, shifting experimentation from the wet lab to the GPU cluster.
We also use large pretrained foundation models to predict, from a simple H&E image, whether a patient is likely to respond to a given drug. This allows us to mathematically define the patient population where a drug will work, effectively solving the translation problem before the trial even begins.
2. As AI adoption in biotech accelerates, where do you see the biggest impact in the next 2–3 years?
AI in biology is moving from the era of molecule design to the era of simulating biology. The last few years were defined by breakthroughs like AlphaFold and structure based design. The next 2–3 years will focus on simulating biological systems using foundation models trained on real patient biology.
The biggest impact will come when models move beyond static predictions and begin functioning as dynamic simulators. These systems won’t just identify targets, they will predict what happens downstream when you perturb those targets in a messy, complex human environment.
The winners in biology won’t simply have the best algorithms. They’ll have the best data. Public datasets aren’t enough to train frontier biological models. We need to generate data that is fit for purpose.
3. What’s one insight or preview you’re excited to share with the PMWC audience in your upcoming talk?
We’re excited to show what happens when biology is treated not as a static snapshot, but as a programmable system. We’ve been developing OCTO-vc, our Virtual Cell capability, which allows us to take a virtual cell, such as a T cell, and place it into different regions of a patient’s tumor to observe how it behaves.
This extends to larger multicellular structures, and we can run these simulations at scale across hundreds of unique patient samples, testing hypotheses directly in human tissue.
We’ve also built industry first capabilities that turn a simple clinical H&E into a powerful predictive biomarker across multiple therapeutic mechanisms using multimodal foundation models. We’re validating this work with partners like Agenus, while also applying it internally to build our own clinical stage programs.
We’re excited to share these capabilities at PMWC, and potentially preview new ones we haven’t shared publicly before.
