Propose
Models generate candidate sequences, structures, and pathways.
Science
Reading, writing, and editing the code of life.
Biological sequence behaves like language. We train foundation models on it to predict, design, and engineer biology.
DNA / RNA
Genome foundation model
We treat genomes as text and train models that find genes, regulatory elements, and novel ORFs at scale.
- Gene prediction and annotation
- Novel coding and regulatory elements
- Metagenome and pan-genome representation
Protein
Protein foundation model
The protein universe is a finite set of folds. We learn them, then design enzymes and binders toward a target function.
- Structure-aware representation
- Enzyme and biomolecule design
- Folds read in the light of evolution
Genomic LLM
Biosequence language model
One model reasoning over DNA, RNA, and protein — signals crossing modalities, where much of the useful biology lives.
DMBTL
Models that improve every cycle.
Design, model, build, test, learn. Each measured result trains the next generation.
Validate
Candidates are synthesized and assayed through partners.
Update
Outcomes retrain the models, raising accuracy each iteration.
MSIT · National GPU Program
Training at national scale
fablessBio participates in the National GPU Support Program of the Ministry of Science and ICT (MSIT), which provides the large-scale compute to train and evaluate our foundation models.