Tools · Custom Modeling

Reaction Models

Predict reaction yield, selectivity and rates from atom-mapped reactions and their conditions

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.

Reaction Model Training: reservation per run, USD
Hardware	Typical runtime	Basic	Starter	Pro	Enterprise
CPUDefault	1 h	$0.50	$0.29	$0.18	$0.11
Nvidia T4	1 h	$2.05	$1.48	$1.19	$0.90
Nvidia L4	1 h	$2.81	$2.02	$1.58	$1.19
Nvidia A10	1 h	$3.85	$2.74	$2.20	$1.66

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)CSV, TSV, JSON

Outputs

ArchiveZIP
Trained modelOPAQUE

Ready-made workflows that use it

Train a reaction yield model from your reactionsReactions → atom-map → train a reaction modelPredictive modeling

References

The method behind it.

Connor W. Coley, William H. Green, Klavs F. Jensen (2019). RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.9b00286 (opens in a new tab)
Esther Heid, Kevin P. Greenman, Yunsie Chung et al. (2023). Chemprop: A Machine Learning Package for Chemical Property Prediction. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.3c01250 (opens in a new tab)
Esther Heid, William H. Green (2021). Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.1c00975 (opens in a new tab)
Derek T. Ahneman, Jesús G. Estrada, Shishi Lin et al. (2018). Predicting reaction performance in C–N cross-coupling using machine learning. Science. doi:10.1126/science.aar5169 (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-reaction-mpnn", "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-reaction-mpnn-1", "job_type": "train-reaction-mpnn", "resource": "cpu", "payload": { ... }}'

Payload fields and examples in the docs

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All tools

Price per run

Chain it with other steps.

Ready-made workflows that use it

The method behind it.

Submit it from your own code.

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