# Run virtual screening with AutoDock Vina on Deadline Cloud
<a name="examples-jb-virtual-screening"></a>

The [virtual\_screening\_vina](https://github.com/aws-deadline/deadline-cloud-samples/tree/mainline/job_bundles/virtual_screening_vina) job bundle uses [AutoDock Vina](https://github.com/ccsb-scripps/AutoDock-Vina) to screen large compound libraries against a protein target. The bundle splits a compound library into chunks and docks them in parallel across a fleet of workers, then aggregates and ranks the results by binding affinity.

The pipeline runs four steps:

1. **PrepareReceptor** — Converts the protein PDB to PDBQT format with Open Babel.

1. **SplitLibrary** — Splits the SDF compound library into N chunks.

1. **DockCompounds** — Runs N parallel docking tasks (one per chunk). Each task is idempotent and safe for Spot preemption.

1. **ScoreAndRank** — Aggregates all chunk results into a ranked CSV of top hits.

The bundle requires a Linux x86\_64 service-managed fleet (Spot recommended), a conda queue environment with `openbabel` from `conda-forge`, and AutoDock Vina. Install Vina by building the [Build an AutoDock Vina conda package for Deadline Cloud](examples-conda-autodock-vina.md) conda recipe into your S3 channel, or by installing the binary through a fleet host configuration script.

Set `CompoundLibrary=chembl` (default) to automatically download and filter drug-like molecules from [ChEMBL](https://www.ebi.ac.uk/chembl/), or provide your own SDF file.

From the `job_bundles` directory, submit the job:

```
deadline bundle submit virtual_screening_vina \
  -p "ReceptorPdb=receptor.pdb" \
  -p "CompoundLibrary=chembl" \
  -p "MaxCompounds=100000" \
  -p "CompoundsPerChunk=100" \
  -p "CenterX=-10.7" \
  -p "CenterY=12.4" \
  -p "CenterZ=68.8"
```

For details on building the AutoDock Vina conda package, see [Build an AutoDock Vina conda package for Deadline Cloud](examples-conda-autodock-vina.md).