使い方
- インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
- 必要なツール、認証情報、対応環境を原文で確認します。
- 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/nvidia-bionemo/bionemo-agent-toolkit --skill rfd4-proteina-generate開始前の確認
- 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
- スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
見出し、例、コード、表、リンク、参照画像を含む原文を表示しています。
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.shFix 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 |
| Folding / inverse folding | annotated CIF (backbone-only vs C_SEQ) | tasks/folding-inverse.md |
| Motif scaffolding | motif_scaffolding/motif_indexed, motif_scaffolding/motif_unindexed, motif_scaffolding/motif_indexed_internal_coords | tasks/motif.md |
| Enzyme design (active site) | mcsa/mcsa_37, mcsa/mcsa_37_fixed_ligand, mcsa/mcsa_37_no_ligand | tasks/enzyme.md |
| Small-molecule binder | small_molecule_binder/sm_binder, small_molecule_binder/SAM_hbonds.cif | tasks/sm-binder.md |
| Protein binder | ppi/ppi_all_atom/ | tasks/protein-binder.md |
| DNA binder | dna_binder/dna_binder_denovo_val | 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:
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 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).
# 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/monomersScheduler 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. 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.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.
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 · outputs · per-task guides under 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.
