16/04/2026
Our Representation Layer Extends to a New Domain: 8 Therapies
Alphabiome extends its reference-free intelligence layer to a new domain, turning raw genetic signal into response prediction across three indications and 8 therapies.
The first AI
representation layer for the
unknown genetic universe
Alphabiome learns directly from raw DNA and RNA sequencing data- without reference genomes, predefined features or known biological annotations.
AI can only learn from biology it can represent
Most biological AI begins after humans have already mapped the sequence to known genes, species, proteins or annotations. Everything outside that map is compressed, discarded or treated as noise.
Alphabiome starts earlier – from the raw genetic signal itself.
Biology has a
hidden vocabulary.
We found it.
Our reference-free engine organizes recurring genetic fragments into a global vocabulary of more than 100,000 canonical biological tokens.
Not genes, species or human annotations, but structure discovered in the data, reusable across organisms, tissues and datasets.
Search the unknown. Predict outcomes.
Reveal mechanisms.
One reusable representation, three things it lets you do
Search the unknown
Interrogate genetic signal that existing systems cannot map
Predict outcomes
Build new predictive models on the same reusable representation
Reveal mechanisms
Trace predictive signals back to protein sequences associated with why biological interventions work, or fail
Search the unknown
Interrogate genetic signal that existing systems cannot map
Predict outcomes
Build new predictive models on the same reusable representation
Reveal mechanisms
Trace predictive signals back to protein sequences associated with why biological interventions work, or fail
One engine. Radically different biology.
Consistent signal.
The same core representation has found predictive signal across species, continents, therapies and biological questions. Human performance was validated on held-out clinical data and subsequently confirmed in prospectively collected patients.
Where conventional representations found no predictive signal, Alphabiome did.
On the same human datasets and evaluation framework, Alphabiome outperformed every genomic and microbiome representation we tested, including foundation-model, ORF, abundance and conventional k-mer approaches.
Across all seven IBD therapies, predictive performance exceeded AUC 0.85, with up to threefold responder enrichment compared with established clinical markers.
Prediction is only the beginning
Alphabiome traces high-value predictive signals back to protein sequences strongly associated with response and non-response, turning invisible raw signal into interpretable biological mechanisms
Every dataset makes the representation
harder to replicate.
New biological data expands the vocabulary. A richer vocabulary strengthens the representation. Stronger representations reveal better predictive and mechanistic signal. Better results attract higher-value datasets and partners. The result is a compounding biological intelligence layer, not a collection of one-off models.
Built by AI researchers and world-leading scientists.
Alphabiome brings together AI, mathematics and Nobel Prize-winning biology, with scientific leadership from MIT and Stanford. Patented and patent-pending technology, validated in peer-reviewed studies and human clinical data.
- Member of NVIDIA Inception
Science & News
06/05/2025
AI That Predicts Drug Response from Raw Genetic Signal
Health Technology Net- Alphabiome’s engine reads raw, unannotated DNA beyond reference genomes, delivering unmatched signal for AI drug-response prediction.
06/05/2025
Alphabiome Unveils Revolutionary AI for Reference-Free Decoding
Alphabiome tokenizes raw genetic sequence that today’s biology AI can’t read, to accurately predict response.
21/11/2025
Peer-Reviewed: Reference-Free AI Validated at Scale
A peer-reviewed study validated Alphabiome’s reference-free AI across 10 farms and 339 animals, predicting response directly from raw DNA signal.
07/05/2025
Alphabiome Raises $8M to Build the Biological AI Layer
Finsmes.com- Alphabiome secures seed funding to expand R&D and build the representation layer between raw genetic signal and biological AI.
04/02/2026
Clinical Study: Our AI Predicts Response Across 7 Biologics
In a leading-hospital study, Alphabiome’s AI predicted response across seven biologic drugs, showing up to 3x higher accuracy than state-of-the-art models.
25/09/2025
Independent Validation: Our Reference-Free AI Predicts Again
A controlled trial validated Alphabiome’s reference-free AI on a second compound- an independent test of the same engine on new raw sequence.
25/11/2025
Predicting Response Straight from Raw DNA Signal
Alphabiome’s reference-free AI now reaches human disease. Our tokenization engine reads raw DNA the rest of biology AI can’t, then predicts response.
10/04/2025
Peer-Reviewed Validation: Cross-Farm Prediction Across 13 Herds
A peer-reviewed study validated Alphabiome’s reference-free AI across 13 commercial dairy herds, predicting additive response directly from raw genetic signal alone.
08/09/2024
From Swarm Math to Biology: Predicting Response from Raw Sequence
Our R&D team carries decentralized swarm mathematics into genomics, predicting feed additive efficacy from rumen sequencing, in CRC Press’s Applied Swarm Intelligence
09/01/2024
The Biological Intelligence Layer for AI
Our white paper sets out the thesis behind Alphabiome, tokenizing raw reference-free DNA into the biological intelligence layer today’s biology AI cannot read.
Building the biological data layer with the world’s leading organizations.
