Skip to main content
All tools

Tools · Computational Biology

BoltzGen

Design proteins and peptides to bind biomolecular targets

Open in the appRun via API

Hardware and price

Price per run

What a typical run reserves from your wallet: the catalog's expected runtime on each piece of hardware × your plan's per-second rate. Larger inputs reserve more, and you're charged only for the seconds the job actually runs.

BoltzGen: reservation per run, USD
HardwareTypical runtimeBasicStarterProEnterprise
Nvidia T41 h$2.05$1.48$1.19$0.90
Nvidia L41 h$2.81$2.02$1.58$1.19
Nvidia A10Default1 h$3.85$2.74$2.20$1.66
Nvidia L40S1 h$6.84$4.90$3.89$2.92
Nvidia A100, 40 GB1 h$7.34$5.26$4.21$3.17
Nvidia A100, 80 GB1 h$8.75$6.26$5.00$3.74
Nvidia H1001 h$13.82$9.86$7.88$5.94
Nvidia H2001 h$15.88$11.34$9.07$6.80
Nvidia B2001 h$21.89$15.62$12.49$9.40

In workflows

Chain it with other steps.

Each input and output has a data kind, so the builder only connects steps that fit.

Inputs

None

Outputs

  • ArchiveZIP
  • StructuresPDB

References

The method behind it.

  1. Hannes Stark, Felix Faltings, MinGyu Choi et al. (2025). BoltzGen: Toward Universal Binder Design. openRxiv. doi:10.1101/2025.11.20.689494 (opens in a new tab)

Run via API

Submit it from your own code.

Use an API key from your account. The estimate uses your plan's rates; submitting reserves that amount from your wallet.

# Estimate the reservation for your plan (payload fields: see the API reference)
curl -X POST https://api.cognichem.com/api/v1/jobs/estimate \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"job_type": "boltzgen", "resource": "a10", "payload": {}}'

# Submit (retrying with the same Idempotency-Key never submits twice)
curl -X POST https://api.cognichem.com/api/v1/jobs/submit \
  -H "X-Api-Key: $COGNICHEM_API_KEY" \
  -H "Idempotency-Key: $(uuidgen)" \
  -H "Content-Type: application/json" \
  -d '{"job_name": "boltzgen-1", "job_type": "boltzgen", "resource": "a10", "payload": { ... }}'
Payload fields and examples in the docs

Computational Biology

Related tools

  • Boltz-2

    Accurate in silico screening for early-stage drug discovery

  • DiffDock

    Protein-ligand docking with diffusion models and confidence-ranked poses

  • DrugFlow

    Generate candidate ligands from a protein target and reference ligand using flow matching

  • Sequence Mutator

    Apply point mutations such as T315I to a protein sequence, checking each wild-type residue

  • ESMFold2

    Predict protein, DNA, RNA, and ligand complex structures with evolutionary scale modeling

  • Evo 2

    Genome modeling and design across all domains of life with the Evo 2 DNA language model

All tools