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Custom ADMET Models

Adapt ADMET-AI predictions to your own assay: recalibrate an endpoint onto your units, or stack a model on all 41 predictions

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.

Custom ADMET Model Training: reservation per run, USD
HardwareTypical runtimeBasicStarterProEnterprise
CPUDefault10 min$0.084$0.048$0.03$0.018

In workflows

Chain it with other steps.

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

Inputs

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

Outputs

  • ArchiveZIP
  • TableCSV
  • Trained modelOPAQUE

References

The method behind it.

  1. Kyle Swanson, Parker Walther, Jeremy Leitz et al. (2024). ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics. doi:10.1093/bioinformatics/btae416 (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": "train-admet-custom", "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": "train-admet-custom-1", "job_type": "train-admet-custom", "resource": "cpu", "payload": { ... }}'
Payload fields and examples in the docs

Custom Modeling

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