Multimodal reasoning LLMs trained on biology tasks with expert RL feedback. Not just predicting function — reasoning through evidence the way a PhD scientist would.
Combines ESM2 protein embeddings, AlphaFold structural data, and biological literature into a single reasoning context. Not three models — one chain.
PhD biologists rate reasoning chains, not just predictions. The model learns what constitutes sound scientific thinking — not pattern-matching to training data.
Every prediction comes with a human-readable reasoning trace: which domains, sequences, and evidence the model used. Auditable. Citable. Trustworthy.
ProteinGPT, ESM, AlphaFold — these are exceptional at structure and function prediction. But they are descriptive, not explanatory. They tell you what a protein does. They don't tell you why, or how, or what the evidence for that conclusion is.
A biologist doesn't just classify — they trace evidence across sequence, structure, literature, and mechanistic context. AxiomBio trains models to replicate that chain of reasoning, then uses expert biologist feedback to reinforce it.
Protein function is the first task. The reasoning architecture we're building extends to full cellular context — predicting how a protein behaves in a specific cellular environment, how it interacts with other molecules, how it responds to perturbation.
When we can model a virtual cell with mechanistic reasoning instead of statistical pattern-matching, we change what drug discovery looks like. Every experiment faster. Every hypothesis more grounded. Every failed candidate understood before it's synthesized.