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name: design-pipeline description: "Install prerequisites, select BindCraft design and filter presets, construct a CUDA direct or SLURM launch, and operate or resume the AF2/MPNN validation campaign." disable-model-invocation: true metadata: disco-role: operating license: MIT

Run the BindCraft design pipeline

Use this route after target-preparation has produced a target-settings JSON. It covers the long-running GPU campaign, not PDB preprocessing or downstream binder selection. Use results-analysis for output interpretation.

Route

1. Establish a Python 3.10 environment with a CUDA-capable NVIDIA GPU, a compatible JAX/JAXLIB CUDA build, ColabDesign, PyRosetta, scientific Python dependencies, a readable AF2 parameter bundle (about 5.3 GB), and usable DSSP and DAlphaBall executables. PyRosetta licensing is a separate legal gate; commercial use requires the applicable license. Do not treat a CPU environment as a substitute for this pipeline.

2. Confirm that the target JSON has been validated and that design_path is writable. Select an advanced preset and a filter preset; copy/adapt JSONs rather than silently editing a shared preset. See advanced-settings and filter-settings.

3. Check the AF2 parameter directory, dssp_path, and dalphaball_path in the advanced settings. Empty paths are resolved by BindCraft's own defaults; verify those defaults on the installation being used rather than assuming a checkout-relative path.

4. Build a command without executing it:

   python skills/disco/bindcraft/sub-skills/design-pipeline/scripts/build_bindcraft_command.py \
     --mode direct --settings ./settings_target/my_target.json \
     --filters ./settings_filters/default_filters.json \
     --advanced ./settings_advanced/default_4stage_multimer.json --dry-run
   

   Use --mode slurm and scheduler resource overrides for a batch launch. The builder only prints a shell-safe command; it never submits or runs one.

5. Launch with either the exact direct or SLURM forms in launching. Keep one campaign per design_path; do not run concurrent processes against the same CSVs and output folders.

6. Monitor trajectory acceptance, failure_csv.csv, GPU memory, and disk growth. The loop stops when the requested accepted count is reached, when max_trajectories is reached, or when the enabled acceptance-rate monitor decides that the campaign is underperforming. It is target-dependent and may require hundreds or thousands of trajectories; a short smoke test is not evidence of useful binder generation.

7. To continue a campaign, relaunch with the same target design_path and compatible settings after checking the existing CSV/PDB state. BindCraft has no separate --resume flag: existing trajectory names and accepted files are used to avoid some duplicate work, while the loop can add new random trajectories. Preserve the settings/filters/advanced files used by the campaign.

Detailed stage gates and artifacts are in pipeline; launch flags and scheduler resources are in launching; failure recovery is in troubleshooting.

Safety boundary

The design loop calls AF2 backpropagation, MPNN sampling, AF2 complex and monomer prediction, PyRosetta relaxation, and structural scoring. It requires CUDA and external weights and can run for hours or days. Do not promise CPU execution, affinity, or a successful target-dependent outcome. Do not bundle an installer or download helper here; installation and weight acquisition require user-controlled network, license, storage, and environment decisions.