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Active Learning

Pick the next batch of compounds to make from your measurements, balancing predicted value, uncertainty and diversity

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.

Active Learning: reservation per run, USD
HardwareTypical runtimeBasicStarterProEnterprise
CPUDefault5 min$0.042$0.024$0.015$0.009

In workflows

Chain it with other steps.

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

Inputs

  • Molecules (list) · optionalSMILES
  • Table (list) · optionalCSV, TSV, JSON
  • Table (list) · optionalCSV, TSV, JSON
  • Trained model · optionalOPAQUE

Outputs

  • ArchiveZIP
  • MoleculesSMILES
  • TableCSV

Ready-made workflows that use it

References

The method behind it.

  1. Liva Ralaivola, Sanjay J. Swamidass, Hiroto Saigo et al. (2005). Graph kernels for chemical informatics. Neural Networks. doi:10.1016/j.neunet.2005.07.009 (opens in a new tab)
  2. Donald R. Jones, Matthias Schonlau, William J. Welch (1998). Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization. doi:10.1023/a:1008306431147 (opens in a new tab)
  3. Niranjan Srinivas, Andreas Krause, Sham M. Kakade et al. (2012). Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Transactions on Information Theory. doi:10.1109/tit.2011.2182033 (opens in a new tab)
  4. David Ginsbourger, Rodolphe Le Riche, Laurent Carraro (2010). Kriging Is Well-Suited to Parallelize Optimization. Computational Intelligence in Expensive Optimization Problems. doi:10.1007/978-3-642-10701-6_6 (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": "active-learning", "resource": "cpu", "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": "active-learning-1", "job_type": "active-learning", "resource": "cpu", "payload": { ... }}'
Payload fields and examples in the docs

Custom Modeling

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