Tools · Custom Modeling
Target Rescorer
Learn a target-specific score from Vina, GNINA, Boltz-2, MM-GBSA and PLIP signals on your measured compounds, and see which signals predict activity
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.
| Hardware | Typical runtime | Basic | Starter | Pro | Enterprise |
|---|---|---|---|---|---|
| CPUDefault | 10 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
- Structures (list) · optionalCIF, PDB
- Docked poses (list) · optionalSDF
- Table (list) · optionalCSV, TSV, JSON
- Docked poses (list) · optionalSDF
- Table (list) · optionalCSV, TSV, JSON
- Table (list) · optionalCSV
- Table (list) · optionalCSV
- Docked poses (list) · optionalSDF
- Docked poses (list) · optionalPDBQT, SDF
Outputs
- ArchiveZIP
- TableCSV
- TableCSV
- TableCSV
- Trained modelOPAQUE
Ready-made workflows that use it
- Train a target rescorer on your measured compoundsVina + GNINA + PLIP → target rescorerVirtual screening
References
The method behind it.
- Jean-François Truchon, Christopher I. Bayly (2007). Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem. Journal of Chemical Information and Modeling. doi:10.1021/ci600426e (opens in a new tab)
- Cheng Wang, Yingkai Zhang (2017). Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. Journal of Computational Chemistry. doi:10.1002/jcc.24667 (opens in a new tab)
- Scott M. Lundberg, Gabriel Erion, Hugh Chen et al. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence. doi:10.1038/s42256-019-0138-9 (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-rescorer", "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-rescorer-1", "job_type": "train-rescorer", "resource": "cpu", "payload": { ... }}'Custom Modeling
Related tools
- MPNN Models
Fast structure-property inference that highlights key molecule substructures
- QSAR Models
Compare random forest, LightGBM, Gaussian process and other QSAR models on your data and keep the best
- Active Learning
Pick the next batch of compounds to make from your measurements, balancing predicted value, uncertainty and diversity
- Reaction Models
Predict reaction yield, selectivity and rates from atom-mapped reactions and their conditions
- Thermo Property Models
Correct Joback and Hansen estimates with your own measurements and predict solubility and mixture properties
- Python Script Sandbox
Short RDKit, pandas, NumPy and SciPy analyses of your results, run in isolation with no network access