Tools · Computational Biology
AutoDock Vina
High performance molecular docking and virtual screening for drug discovery
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 | 15 min | $0.13 | $0.072 | $0.045 | $0.027 |
| Nvidia T4 | 10 min | $0.34 | $0.25 | $0.20 | $0.15 |
| Nvidia L4 | 10 min | $0.47 | $0.34 | $0.26 | $0.20 |
| Nvidia A10 | 10 min | $0.64 | $0.46 | $0.37 | $0.28 |
| Nvidia L40S | 10 min | $1.14 | $0.82 | $0.65 | $0.49 |
| Nvidia A100, 40 GB | 10 min | $1.22 | $0.88 | $0.70 | $0.53 |
| Nvidia A100, 80 GB | 10 min | $1.46 | $1.04 | $0.83 | $0.62 |
| Nvidia H100 | 10 min | $2.30 | $1.64 | $1.31 | $0.99 |
| Nvidia H200 | 10 min | $2.65 | $1.89 | $1.51 | $1.13 |
| Nvidia B200 | 10 min | $3.65 | $2.60 | $2.08 | $1.57 |
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 · optionalJSON
- Molecules (list)SDF, MOL, MOL2, PDBQT
- Protein structurePDB
Outputs
- ArchiveZIP
- Docked posesPDBQT
Ready-made workflows that use it
- Screen a compound library against a targetSMILES → 3D embed → dockVirtual screening
- Dock analogs enumerated from your SAR dataFree Wilson → 3D embed → dockLibrary design
- Grow analogs from your SAR table with matched pairs, then dockMatched pairs → analogs → dockLibrary design
- Generate and dock new ligands for a pocketDrugFlow generate → dockLibrary design
- Dock against a protein with no solved structureSequence → fold → dockStructure prediction to docking
- Screen an ultra-large combinatorial libraryThompson Sampling → 3D embed → dockVirtual screening
- Rescore docking hits with a CNN scoring functionVina triage → GNINA rescoreVirtual screening
- Re-predict docking hits with Boltz-2 affinitiesVina triage → Boltz-2 re-predict (batch)Virtual screening
- Re-dock top hits with surface-aware diffusionVina triage → SurfDock (scatter)Virtual screening
- Dock into a cleaned predicted structure and see the contactsSequence → fold → prepare → dock → PLIPStructure prediction to docking
- Compare docking against a wild type and its point mutantsPoint mutants vs wild type → fold → dock → compareStructure prediction to docking
- Build a library from building blocks, filter it, then dockReaction enumerate → filter → diversity pick → dockLibrary design
References
The method behind it.
- Oleg Trott, Arthur J. Olson (2009). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry. doi:10.1002/jcc.21334 (opens in a new tab)
- Jerome Eberhardt, Diogo Santos-Martins, Andreas F. Tillack et al. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling. doi:10.1021/acs.jcim.1c00203 (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": "autodockvina", "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": "autodockvina-1", "job_type": "autodockvina", "resource": "cpu", "payload": { ... }}'Computational Biology
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- DrugFlow
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- Sequence Mutator
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- ESMFold2
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