---
name: rfd4-proteina-generate
description: Use when generating RFD4-Proteina designs from annotated inputs with task, sampling, batch, and inline-metric choices.
license: CC-BY-4.0
allowed-tools: Bash, Read, Write
metadata:
  author: Ohad Mosafi <omosafi@nvidia.com>
  tags: [protein-design, rfd4, generate]
---

# 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
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:

| Design task | Example input(s) in `data/benchmarks/` | Reference |
| --- | --- | --- |
| Unconditional monomer | `monomer`, `adherence/monomer_helical`, `adherence/monomer_ig` | [tasks/unconditional.md](references/tasks/unconditional.md) |
| Folding / inverse folding | annotated CIF (backbone-only vs `C_SEQ`) | [tasks/folding-inverse.md](references/tasks/folding-inverse.md) |
| Motif scaffolding | `motif_scaffolding/motif_indexed`, `motif_scaffolding/motif_unindexed`, `motif_scaffolding/motif_indexed_internal_coords` | [tasks/motif.md](references/tasks/motif.md) |
| Enzyme design (active site) | `mcsa/mcsa_37`, `mcsa/mcsa_37_fixed_ligand`, `mcsa/mcsa_37_no_ligand` | [tasks/enzyme.md](references/tasks/enzyme.md) |
| Small-molecule binder | `small_molecule_binder/sm_binder`, `small_molecule_binder/SAM_hbonds.cif` | [tasks/sm-binder.md](references/tasks/sm-binder.md) |
| Protein binder | `ppi/ppi_all_atom/` | [tasks/protein-binder.md](references/tasks/protein-binder.md) |
| DNA binder | `dna_binder/dna_binder_denovo_val` | [tasks/dna-binder.md](references/tasks/dna-binder.md) |

**Need an input the request doesn't have?** Annotated CIF is the recommended,
lossless format. When the task needs coordinate/sequence conditioning, hotspots,
fixed ligands, H-bonds, RASA, or unindexed motifs, **hand off to
rfd4-proteina-annotated-cif** to build and validate the CIF, then come back and
pass it via `inputs=<file>.cif`.

## Step 3 — Run

General form:

```bash
rfd4-generate inputs=<file> num_replicates=<N> out_dir=<dir> [overrides...]
```

Use the Python environment activated during setup. The installed command runs
in that environment; see the [installation guide](../rfd4-proteina-setup/references/environment.md)
for environment and scheduler settings.

**The four parameters you almost always set** (defaults are only right for a
smoke test):

| Parameter | What to set it to | Default |
| --- | --- | --- |
| `out_dir` | a real output path | `./runs/<run_name>` |
| `num_replicates` | designs per example (e.g. 8-80); `1` is a smoke test | 1 |
| `batch_size` | start at 8, but first confirm the longest input fits at `batch_size=1` | 1 |
| `metrics_tags` | inline metrics as a bracket-quoted Hydra list; only refolding tags need a container | `[]` |

Useful extras: `subset_to_examples=monomer-short` (run only named keys),
`seed=<int>` (reproducibility), `skip_existing=true` (resume a run),
`save_chain_of_interest=true` (required for later foldseek clustering).

```bash
# 80 designs of each monomer example, 8 at a time
rfd4-generate \
    inputs=data/benchmarks/monomer \
    num_replicates=80 batch_size=8 out_dir=./runs/monomers
```

## Scheduler intent

For any batch run, read the selected `scripts/.user_info_<cluster>` and the
launcher before constructing the command. Preserve the user's partition,
account, QoS/priority options, GPU request, runtime, memory, and CPU settings.
Use a resource override only when the user explicitly requested it. State the
resolved scheduler values before submission; use dry-run or non-submitting tests
when validating the workflow.

## Step 4 — Pick your overrides

Full cheat sheet: [references/overrides.md](references/overrides.md). The knobs
that matter most:

- **Diversity (stochasticity).** Backbone temperature
  `generation.model.bb_ca.simulation_step_params.sc_scale_noise` (default 0.3)
  and sidechain/sequence temperature
  `generation.model.local_latents.simulation_step_params.sc_scale_noise`
  (default 0.1). Roughly: `(0.1,0.1)` = high designability, `(0.5,0.5)` =
  natural UniProt-like sequences, `(0.9,0.9)` = maximum diversity.
- **Partial diffusion (refine an existing structure).** Simple:
  `generation.args.partial_trajectory_fraction_steps=<f>` (fraction of `nsteps`).
  Advanced: `generation.args.partial_t_backbone=<t>` /
  `generation.args.partial_t_latent=<t>` (floats in `(0,1)`; near 1 stays close
  to the input, near 0 approaches full generation), optionally
  `generation.args.couple_partial_t=true`. `partial_t` is a corruption level, not
  Angstroms.
- **Deterministic (ODE) sampling** (no mid-trajectory noise):
  `generation.model.bb_ca.gt.mode=ode generation.model.local_latents.gt.mode=ode`.
- **Steps / recycling.** The default 1x schedule uses 60 model evaluations
  and 185 trunk passes, with twice the integration-step density in the final 20%
  of each modality's clock. Select `recycle_strategy=thinking_high` for the
  1.5x recycle-depth variant.

## Step 5 — Inline metrics (optional)

Pass `metrics_tags='[...]'` to compute metrics right after each design; results
land in `per_example_metrics.csv` alongside the CIFs. Common tags include `design`, `unconditional_refolding`, `ppi_refolding`,
`dna_refolding`, `motif_scaffold_refolding`, `sm_binder_refolding`,
`ame_refolding`, `disco_refolding`, `hotspot_conditioning`,
`3d_coordinate_conditioning`, `rasa_conditioning`, and
`interchain_interactions`. Check `docs/workflow/metrics.md` and the composed
metric configs before treating any list as exhaustive.

> [!NOTE]
> Every `*_refolding` tag runs the **AF3 oracle**, which needs an apptainer/docker
> container (see **rfd4-proteina-setup** for the container, **rfd4-proteina-evaluate**
> for what the metrics mean and how to read them). Plain generation needs no
> container. Deep metric detail belongs to **rfd4-proteina-evaluate** — this skill
> only turns the tags on.

```bash
rfd4-generate \
    inputs=data/benchmarks/monomer num_replicates=50 batch_size=8 \
    out_dir=./runs/monomers_scored metrics_tags='[unconditional_refolding]'
```

## Reward-guided / beam search

For "generate many, keep the best under an AF3 reward", do **not** hand-roll it
here — use **rfd4-proteina-search**, which drives
`configs/inference_experiment/inference_beam_example.yaml` and the search config
knobs (beam width, branching, checkpoints, metric weights).

## Reusable configs

For a repeatable experiment instead of one-off CLI overrides, copy
`configs/inference_experiment/TEMPLATE.yaml` — it documents the three ways to run
(in-tree experiment config, CLI overrides, config file anywhere on the
filesystem). The default group is `inference_on_user_inputs`.

## Outputs

Each design is an **annotated CIF** with per-atom provenance columns (`SA_SRC`,
`SA_CRDSRC`, `SA_IDXGRPSRC`, `SA_UII`) tying output atoms back to the input spec.
With metrics on, a `per_example_metrics.csv` is written alongside. Full column
table and how to read annotations: [references/outputs.md](references/outputs.md).

## Troubleshooting

| Symptom | Fix |
| --- | --- |
| Out of memory | Lower `batch_size`; confirm your longest input fits at `batch_size=1` first |
| `--inputs`/`--out_dir` not recognized / Hydra config error | Use `=` not `--`: `inputs=file.cif`, never `--inputs file.cif` |
| checkpoint not found | Confirm `DATA_PATH`, then compare `ckpt_name` and `ckpt_path` with `configs/inference.yaml`; override only when intentionally using another checkpoint |
| `DATA_PATH` / checkpoint not found | `export DATA_PATH=...` then `./scripts/download_example_data.sh "$DATA_PATH"` |
| `atomworks not found` | Re-run `make setup` to install the public AtomWorks dependency |
| `hydra not found` / import errors | `source .venv/bin/activate` |
| Missing runtime data-path or cluster configuration | Follow the setup environment reference for the selected checkout; preserve the requested cluster and scheduler settings. Export `DATA_PATH` in the process environment for local use. |
| AF3 / `*_refolding` errors | Refolding metrics need an AF3 container — see rfd4-proteina-setup + rfd4-proteina-evaluate |
| Segfault in rdkit/posebusters | Verify the supported Python and C++ runtime versions using the setup environment reference. Use the checkout-maintained launcher for any required ABI workaround. |

For deeper detail: [overrides](references/overrides.md) ·
[outputs](references/outputs.md) · per-task guides under
[references/tasks/](references/tasks/).

## Limitations

A generated CIF verifies the inference path, not folding, binding, or experimental function. Checkpoints, available conditions, and metrics depend on the selected model revision.

## Examples

The Step 3 command generates 80 designs per monomer example. The task table links to conditioned examples; use the user-requested count and validate a single input before scaling.