RFD4-Proteína Generate

Purpose

Single-shot generation for RFD4-Proteína. One entry point drives every design task, unconditional monomers/complexes, folding & inverse folding, motif scaffolding, enzyme design, small-molecule binders, protein binders, and DNA binders. The skill's job is to route the request to the right example input and the right overrides, then run one command. Deep input construction, reward-guided search, and oracle metrics live in sibling skills; this skill points at them.

Run everything from the repository root (the directory containing .project-root). All model arguments are Hydra key=value, never --key value.

Prerequisites

A model environment that passes setup preflight, supported GPU, checkpoints, and a validated annotated input or benchmark example.

Use the user-selected model checkout at RFPROTEINA_ROOT for model commands, configs, and upstream manuals. This catalog contains the skills, not that checkout. In helper examples, resolve open-models-skills/rfd4-proteina/<name>/... to its absolute installed path when the catalog and model checkout are separate.

Inputs

Required: the user's task, input CIF or benchmark path, output directory, and design count. Optional: batch size, seed, sampling overrides, and metric tags. Preserve explicit choices and use the documented defaults only for unspecified values.

Instructions

Step 1, Preflight

Never launch a long run against a broken environment. Confirm venv, GPU, and checkpoints first:

bash open-models-skills/rfd4-proteina/rfd4-proteina-setup/scripts/preflight.sh

Fix every FAIL (see rfd4-proteina-setup) before generating. scripts/download_example_data.sh fetches the release pair composed by configs/inference.yaml, so the default run needs no checkpoint override.

Step 2, Route the task

Pick the design case, then open its reference for the exact command, example input, task-specific overrides, and gotchas: