New drug formulas, from reasoning you can check.
We are connecting with the pharmaceutical industry to find new drug formulas with GitMir's intelligence algorithms that work outside the AI. They build the reasoning outside the model, so every step of it can be validated deterministically — not trusted because it sounds plausible.
These are autonomous, self-learning systems: the reasoning is a living structure of connected meanings that changes as it reasons — the closest thing we know to how the human brain and psyche work.
Our algorithms build the reasoning outside the language model. The model is one component of the system, not the whole intelligence.
Every step of the reasoning is explicit, so the same check gives the same answer every time — a chemist can follow it, test it and reject it.
The systems run their own loops of hypothesis, evidence and experiment, and learn from what held and what did not instead of starting again from raw literature.
The reasoning is a living structure of connected meanings that changes as it reasons — the closest thing we know to how the human brain and psyche work.
Drug repurposing and target ranking as explicit, sourced paths with the weakest step named — and the next experiment chosen to separate competing ideas.
Read the article →Disagreements and hidden links that exist only between papers — found with the sources and conditions on both sides, where a summary would say “mixed results”.
Read the article →GitMir publishes what its work produces and how it is measured. It does not publish the representation or the algorithms behind this line. This is work in progress: when there is a result worth reporting, it will appear here as a measurement, with method, scope and limitations.