Scientific Computing

Your lab's AI shouldn't need a data center.

Models trained and benchmarked · Available for research collaboration

Foundation models for genomics and proteomics are powerful - but they're also 650 million to 2.5 billion parameters. Running them means cloud GPUs, data transfer agreements, and compute budgets that compete with wet lab time.

We build models that are orders of magnitude smaller while matching or approaching the accuracy of full-size foundation models. They run on a workstation. On a laptop. Offline. Your sequences never leave your network.

Protein Language Model

Trained on large-scale protein databases

Protein sequence generation and analysis

Generates biologically valid protein sequences with full amino acid diversity. All 20 standard amino acids represented. No mode collapse.

100x+
Smaller than established models
20/20
Amino acids represented

DNA Genomics Model

Trained on human genome · Evaluated on standardised benchmarks

DNA sequence understanding and classification

Trained on minimal data. Evaluated against significantly larger established models using the same pipeline. Outperforms on multiple genomic tasks.

50x+
Fewer params than comparable models
Multiple
Tasks beating larger models

Validated on real biological features. The model captures genomic structure, not just statistics. Detailed benchmarks available under NDA.

Why this matters for your lab

Runs on your hardware

Workstation GPU, M-series MacBook, even embedded devices. No cloud compute budget. No data transfer agreements.

Your data stays yours

No API calls. No cloud provider sees your sequences. Run air-gapped if your institution requires it.

Custom domain specialists

Train a specialist on your proprietary sequences in minutes. CYP variants, binding sites, expression patterns. Tiny adapter, your data.

Multiple specialists, one model

Drug interaction, clinical trial screening, patient stratification - all from one base model. Switch in milliseconds.

"The model is small enough to run on a $50 Android phone. A village health worker in rural India could run genomic screening offline. A pharma lab can run protein analysis without a cloud contract."

What we can do together

Provide pre-trained models. Protein and DNA foundation models, ready to use. Fine-tune on your data or use as-is for embeddings, classification, generation.

Train on your data. We train custom specialists on your proprietary sequences. You get a compact model file. We never see your raw data - only the trained adapter.

Embed in your pipeline. Our models export to PyTorch, CoreML, ONNX. Drop into your existing bioinformatics pipeline. No new infrastructure.

Research collaboration. Joint publications. Your domain expertise, our compression architecture. We've published in IEEE and collaborate with researchers across genomics, proteomics, and computational biology.

Run foundation models where your data lives.

We work with research labs, pharma R&D, and biotech companies who need AI that respects their data boundaries.

gaurav@nonlinear.technology Research publications →