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Chai-1

EGFR kinase with gefitinib (cancer drug target)

The latest state-of-the-art models. From predicting structures with AlphaFold 2 and Boltz-2 to generating binders with BoltzGen. All running on A100, H200, & B200 GPUs.

AlphaFold2

AlphaFold2

AlphaFold2 via ColabFold for high-accuracy protein structure prediction. Uses MMSeqs2 API for MSA generation with no local databases required. Supports monomer and multimer prediction.

Boltz-2

Boltz-2

Boltz-2 is a biomolecular foundation model for structure and binding affinity prediction. Supports proteins, ligands, DNA, and RNA in multi-component complexes. Automatically scales GPU resources for large complexes. Predicts binding affinity with near-FEP accuracy at 1000x faster speed.

Chai-1

Chai-1

Chai-1 is a multi-modal foundation model for molecular structure prediction. Predicts 3D structures for proteins, ligands, DNA, RNA, and multi-component complexes with high accuracy.

RFdiffusion3

RFdiffusion3

All-atom generative diffusion model for protein design with complex constraints. Design binders, enzymes, and symmetric protein assemblies.

BoltzGen

BoltzGen

BoltzGen is a state-of-the-art AI model for designing protein and peptide binders against any biomolecular target. Using generative diffusion models, it creates novel binders (proteins, peptides, nanobodies) with nanomolar-level binding affinity.

ESMfold

ESMfold

ESMfold is a fast, single-sequence protein structure predictor from Meta AI. Predicts 3D protein structures directly from amino acid sequences without requiring multiple sequence alignments (MSA), making it significantly faster than AlphaFold while automatically scaling GPU resources for larger proteins.

OpenFold-3

OpenFold-3

OpenFold-3 is an open-source AI model for biomolecular structure prediction, aiming to reproduce AlphaFold3. Predicts 3D structures for proteins, RNA, DNA, and small molecule ligands with high accuracy.

RosettaFold3

RosettaFold3

Open-source structure prediction neural network for proteins, nucleic acids, and small molecules. State-of-the-art accuracy with multi-chain support.

Paste a sequence and submit

Pick any tool, paste a FASTA sequence or upload a PDB, adjust settings if you want, and hit Submit. No scripts, no command line.

Input

History
GFP structure prediction

>GFP_Aequorea
MSKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPW...

Prediction parameters
100 / 450
Reset
Submit

Try examples before committing your own input

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Output

ViewerDataFiles
Configure input settings on the left, then click "Submit"orTry an example (it's free)

Human Ubiquitin (76 aa) - Nobel Prize 2004

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Green Fluorescent Protein (238 aa) - Nobel Prize 2008

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Every job saved with full metadata

See what you ran, when it finished, how many credits it cost, and how long it took. Reload any past result in one click.

GFP structure prediction

Completed

2m ago

Input: 238aa · Credits: 100 · Processing: 47.21s

KRAS docking — saquinavir

Processing...

8m ago

Input: 1 ligand · Credits: 20

Insulin fold

Completed

14m ago

Input: 110aa · Credits: 50 · Processing: 12.38s

Ubiquitin prediction

Completed

22m ago

Input: 76aa · Credits: 100 · Processing: 38.54s

Watch your job compute in real time

Elapsed time ticks live while your prediction runs. An estimated time remaining updates as the job progresses.

Output

Computing... (1m 23s)
~2m 15s remaining

Built by scientists for scientists

Trusted by leading institutions

Used by researchers at Harvard, Roche, Thermo Fisher, Cambridge, and 40+ institutions worldwide.

Your data stays yours

Files are processed and deleted. We don't store your sequences or structures beyond your session.

GPU acceleration

Jobs run on NVIDIA B200, H200, & A100 GPUs. Get results in minutes, not days.

No setup, no queues

Upload your data, click run, download results. No installation, no dependencies, no waiting.

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