We are solving the equation of cancer.
To transform oncology from a statistical guessing game into a precise, predictive science by encoding the laws of physics into the heart of Artificial Intelligence.
The Missing Variable
For decades, precision medicine has been fighting with one hand tied behind its back. We sequenced genomes, mapped pathways, and amassed petabytes of data. Yet, we treated cancer as a static snapshot.
"What mutation does this patient have?"
"How will this system evolve over time?"
DNAI was born from a realization: Biology is not just data. It is physics. Cells divide, drugs diffuse, and populations compete according to immutable laws. Standard AI ignores these laws—it just matches patterns. That's why it hallucinates. That's why it fails.
We built DNAI to fix this. We stopped treating the tumor as a "Black Box" and started building a "Glass Box"—a digital twin that respects the biological constraints of the human body.
The Neuro-Symbolic Manifesto
We believe the future of medicine lies at the intersection of two opposing forces. We don't choose between them; we fuse them.
Perception + Reasoning
Deep Learning is the best tool for Perception (seeing the tumor in the data). Physics is the best tool for Reasoning (predicting how it will grow).
DNAI combines them into a single Neuro-Symbolic architecture. Our VAE sees the biology; our ODEs simulate the future.
Mechanism > Correlation
In a dataset, "tumor size" might correlate with "drug response." But correlation isn't mechanism.
Our models use Physics-Informed Constraints to encode biological mechanisms—not just fit patterns. We don't just want to know that a drug works; we demand the physics of why.
Constraints are Safety
A standard AI can predict a negative tumor volume. DNAI cannot.
By hard-coding the laws of mass action and population dynamics into our Neural ODEs, we ensure every prediction is physically possible. In healthcare, physics isn't just a feature—it's a safety guardrail.
The Technology Behind the Mission
We are building the infrastructure for the next century of cancer care.
The Engine
H-BDVAE v5.10Our proprietary H-BDVAE Foundation Model achieves statistical orthogonality (R² < 0.001) between biological identity and growth artifacts. It sees the patient, not the noise.
The Simulator
Neural ODEOur Trajectory Emulator allows us to run thousands of virtual clinical trials in seconds, optimizing dosing schedules to find the mathematical limit of efficacy while respecting toxicity constraints.
The Validation
Evidence-FirstWe don't grade ourselves on easy tests. We validate on held-out cohorts and future time-points, proving our predictions hold up against the arrow of time.
Our Team
Remote-first. Interdisciplinary. Mission-driven.
We are a remote-first, interdisciplinary collective of physicists, oncologists, and machine learning engineers. We are united by a dissatisfaction with the status quo and a drive to solve "hard" problems.
We don't just write code. We read papers on quantitative systems pharmacology. We debate the kinetics of drug resistance. We build tools for the doctors who treat the patients we hope to save.
Join us in decoding the dynamics of life.
We're looking for people who want to work on hard problems that matter.