How it works
The unbiased intelligence that reads any molecule, mechanistically.
Others predict odds from history. infinitusbio's Macro-connectionist Multi-Agent AI digitally clones a patient's cell, treats the clone with the drug, and reads the mechanism: which indications, which patients, and why. Odds tell you how often. Mechanism tells you why.
We lead where the search space is vast and the learning space is sparse.
~7,000
Rare diseases
About 95% have no approved treatment, and there are too few patients for any training dataset. Orphan exclusivity runs 7 years in the US and 10 in the EU.
~500,000
Two-drug combinations
Pairs from 1,000 approved drugs, per disease: C(1,000, 2) = 499,500. Clinical trials test a handful. Each pair is simulated in minutes, patient by patient.
Rare-disease figures: NIH and Global Genes estimates.
A sharper read. A better call.
| Today's diligence | infinitusbio | |
|---|---|---|
| Input | Voluminous data, KOL interviews | A drug's targets plus public patient mRNA |
| Method | Pattern-matching on past trials | Whole-cell mechanistic simulation |
| Bias | Inherits the biases of its history | No training, so no inherited bias |
| Speed | Weeks to months per question | Minutes per run |
| Cost | High, and rising with data volume | A fraction, at machine scale |
| Result | Nine in ten drugs still fail | Better decisions, with the mechanism shown |
Landscape
No one else reads efficacy mechanistically from public data.
| Approach | Method | What it predicts | Minimum inputs |
|---|---|---|---|
| Trial-history ML | ML on curated trial history | Probability of success | Years of curated trial outcomes; blind to new mechanisms |
| Clinical-trial event trackers | AI over trial and regulatory signals | Event signals | Trial and regulatory event feeds; no efficacy simulation |
| Analyst base rates | Analysts and phase averages | Catalyst calendars, phase odds | Phase averages from past drugs, not this drug in these patients |
| Expert networks | KOL opinion | Clinician view on a readout | Access to key opinion leaders; slow, with information-barrier risk |
| Sentiment and LLM models | Press-release tone, prompts | Price reaction after disclosure | Disclosures after the fact; no mechanism shown |
| Patient-data ML response models | ML on patient data | Outcomes, subgroups | Large labeled patient datasets to train on |
| infinitusbio | Digital AI Clones | Primary and new indications, responders, combinations, before capital is committed | Limited inputs from public or proprietary data are sufficient to digitally clone patient cells and biopsies. No training. Fully auditable. |
See it on your molecule.
We can demonstrate non-public combination results on your drug within weeks, under NDA.