Tabula is building an AI-first therapeutics company.
We are starting with bacteriophages, natural predators of bacteria, and building the models and experimental systems needed to design better therapies for hard-to-treat infections. The immediate problem matters on its own. Antibiotic resistance is a large and growing global problem, and new approaches are badly needed. But we also think this is the beginning of something broader.
Most biotech companies are mostly biologists with a small computational team attached. We think that model is going to look dated. Our view is that drug and therapy discovery will become much more computational over time, and we are building Tabula around that belief from day one.
The core of our scientific work is the development of physics-based mathematical models of phage–bacteria interaction dynamics — how phages locate, bind to, infect, and replicate within bacterial targets at the population level — together with the large-scale numerical simulation of those models to identify and refine candidate therapies. To support that modeling, we run an in-house wet lab that generates the experimental data used to calibrate, test, and validate the underlying physical models.
Two things make this a particularly interesting problem. First, we own our data generation loop. We are not just consuming static datasets and hoping they are good enough. We can generate new data, learn from it, and improve the system over time. Second, phages are one of the rare places in biology where the path from model output to human impact can be unusually short. We picked this area in part because, relative to most of biotech, the feedback loop to real-world use is fast.
One way to describe what we are doing is simple: we are using computational and physics-based modeling to help design living therapies that can save lives. That sounds a little like science fiction, which is part of why we think it is worth doing. But it is also a very practical engineering and research problem.