---

name: reinvent4 description: "Use REINVENT4 for molecular design CLI workflows: sampling, scoring, transfer learning, staged reinforcement learning, enumeration, plugins, and SMILES preprocessing." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

REINVENT4

Use this repo skill when a task mentions REINVENT4, reinvent, reinvent_datapre, de novo molecular design, molecular scoring, transfer learning, staged reinforcement learning, peptide enumeration, LibInvent, LinkInvent, Mol2Mol, PepInvent, or REINVENT scoring plugins.

First Checks

1. Confirm the package imports and CLIs are available:

   python scripts/check_reinvent_install.py
   

2. Read references/cli-reference.md when choosing global CLI flags, config formats, logging, seed handling, device overrides, or data-preprocessing CLI syntax.
3. Read references/troubleshooting.md when install/import, Torch/RDKit, CUDA/CPU, optional extras, config parsing, or missing dependency failures appear.
4. Read references/repo-provenance.md before deciding whether this skill matches a current checkout or needs refresh.

Route By Task

• sub-skills/sampling/SKILL.md: sample or generate molecules from priors, TL/RL models, LibInvent scaffolds, LinkInvent warheads, Mol2Mol seeds, or PepInvent inputs.
• sub-skills/scoring/SKILL.md: build scoring-only configs, scoring blocks for RL, transforms, aggregation, custom comp_* plugins, and optional scoring integrations.
• sub-skills/learning/SKILL.md: configure transfer learning, staged RL/curriculum learning, diversity filters, inception memory, TensorBoard outputs, checkpoints, and TL-to-RL workflows.
• sub-skills/data-pipeline/SKILL.md: clean and filter SMILES datasets with reinvent_datapre, validate data_pipeline.toml, and create tiny preprocessing fixtures.
• sub-skills/enumeration/SKILL.md: configure peptide/library enumeration and validate scaffold, warhead, Mol2Mol, PepInvent, and amino-acid-library seed files.

Install And Import Guidance

• Use Python 3.11 or newer, matching the package metadata requirement.
• For REINVENT4 CLI use, install the base package and a PyTorch wheel matching the host backend. Use CPU wheels for portable inspection and smoke checks; use CUDA/ROCm/XPU/MPS wheels only when the host driver/backend is known to support them.
• Optional extras are not needed for normal scoring/sampling/TL/RL config work unless the selected scoring component requires them:
  • chemprop1 or chemprop2 for ChemProp scoring components.
  • openeye for ROCS/OpenEye scoring, which also requires a valid license and package index.
  • isim for iSIM similarity tracking.
• At this repository snapshot, reinvent --help imports plotting code that requires scipy; install scipy if the CLI fails with ModuleNotFoundError: No module named 'scipy'.

Common Command Shapes

reinvent --help
reinvent --device cpu --seed 123 --log-filename run.log config.toml
reinvent -f json --device cpu config.json
reinvent_datapre data_pipeline.toml --log-filename datapre.log

Convert configs without relying on source checkout helpers:

python scripts/convert_config_format.py input.toml output.json
python scripts/convert_config_format.py input.json output.yaml --output-format yaml

Safe Operating Rules

• Validate configs and seed files with bundled scripts before launching expensive sampling, TL, RL, enumeration, or external scoring jobs.
• Run tiny CPU smoke checks before scaling to GPU or long jobs.
• Do not run workflows that require proprietary licenses, external services, model downloads, network calls, or long training unless the user explicitly approves those side effects.
• Keep REINVENT run configs, input SMILES, output CSV/model/checkpoint paths, and TensorBoard log directories explicit and separate per experiment.