---

name: sampling description: "Configure and troubleshoot REINVENT4 sampling runs for Reinvent, LibInvent, LinkInvent, Mol2Mol, and PepInvent generators." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Sampling

Use this sub-skill when a user needs to generate molecules with REINVENT4, validate a sampling config, adapt a prior or trained agent for generation, or debug seed-file/model/device/output issues for Reinvent, LibInvent, LinkInvent, Mol2Mol, or PepInvent.

Read First

• references/workflows.md for choosing the generator mode, preparing seed files, running reinvent, and checking output CSVs.
• references/configuration.md for TOML/JSON/YAML keys, templates, CLI flags, model-file handling, and seed-file conventions.
• references/troubleshooting.md for model/seed mismatches, CUDA fallback, invalid formats, output collisions, expensive generation, and prior availability.
• scripts/validate_sampling_config.py for safe no-run validation before launching generation.

Fast Path

1. Confirm run_type = "sampling" and choose device = "cpu" for portable checks or device = "cuda:0" only when a CUDA PyTorch device is available.
2. Put model_file, num_smiles, output_file, unique_molecules, and randomize_smiles under [parameters].
3. Add smiles_file for LibInvent, LinkInvent, Mol2Mol, and PepInvent seed-based runs; omit it for normal Reinvent de novo sampling.
4. Validate without generation:

   python sub-skills/sampling/scripts/validate_sampling_config.py sampling.toml --model-mode Linkinvent
   

5. Run a small smoke generation before scaling:

   reinvent --device cpu --seed 123 --log-filename sampling.log sampling.toml
   

Scope Boundaries

• This sub-skill owns run_type = "sampling", generator model selection, seed-file conventions, num_smiles, uniqueness/randomization, CLI flags, output sanity checks, and static validation.
• For scoring function design, use the scoring sub-skill.
• For transfer learning or staged reinforcement learning that produces an agent to sample from, use the learning sub-skill, then return here to sample the resulting model.
• For raw SMILES cleanup before creating seed files, use the data-pipeline sub-skill.
• For peptide/molecule enumeration workflows, use the enumeration sub-skill.