/openfold3

name: openfold3
description: >
  Structure prediction using OpenFold3, an open-weights PyTorch reproduction of
  AlphaFold3 from the AlQuraishi Lab.
  Use this skill when predicting protein/nucleic-acid/ligand complex
  structures with an Apache-2.0-licensed AF3 reimplementation.
license: Apache-2.0
category: biomodels
requirements: [gpu]
metadata:
  display-name: OpenFold3
  # github.com/aqlaboratory/openfold-3/blob/main/LICENSE: Apache-2.0. HF model
  # card and gated prompt confirm. verified 2026-06-30
  third_party:
    - kind: weights
      name: OpenFold3
      provider: OpenFold Consortium
      license: Apache-2.0
      terms_url: https://github.com/aqlaboratory/openfold-3/blob/main/LICENSE
    # SKILL.md — `--use-msa-server` DEFAULTS to true (MSA server is
    # api.colabfold.com), so the sequence leaves the machine unless opted out.
    # No published ToS — wiki is the closest data-use reference.
    - kind: service
      name: ColabFold MSA server (api.colabfold.com)
      provider: Steinegger Lab
      info_url: https://github.com/sokrypton/ColabFold/wiki

OpenFold3 Structure Prediction

Prerequisites

| Requirement | Minimum | Recommended | | -------------- | ------- | ----------- | | Python | 3.10+ | 3.11 | | CUDA | 12.1+ | 12.4+ | | GPU VRAM | 24GB | 80GB (H100) | | RAM | 32GB | 64GB | | Disk (weights) | 3GB | - |

How to run

Installation

pip install 'openfold3[cuequivariance]==0.4.1'

The default attention kernel is DeepSpeed `DS4Sci_EvoformerAttention`. If DeepSpeed is unavailable, switch to the cuEquivariance triangle kernels (no build-from-source) by overriding the eval memory settings in `model_config.py` (`use_deepspeed_evo_attention: False`, `use_cueq_triangle_kernels: True`). Some pre-built environments already ship this override; check before re-patching.

Weights

Apache-2.0, ~2.3 GB from HF `OpenFold/OpenFold3`. The repo is **gated** (auto-approval) — accept the access form on the HF model page and authenticate (`huggingface-cli login` or `HF_TOKEN`) before downloading:

export OPENFOLD_CACHE=~/.openfold3
huggingface-cli download OpenFold/OpenFold3 checkpoints/of3-p2-155k.pt \
  --local-dir "$OPENFOLD_CACHE"

`run_openfold` will also auto-download to `$OPENFOLD_CACHE` on first run if egress is open and HF credentials are available (either `HF_TOKEN` or a prior `huggingface-cli login`) with repo access granted. The interactive `setup_openfold` helper exists but prompts on stdin; prefer the explicit download above for non-interactive runs.

Running

export OPENFOLD_CACHE=/path/to/cache
run_openfold predict \
  --query_json=queries.json \
  --output-dir out/ \
  --use-msa-server false \
  --use-templates false

`run_openfold` discovers the checkpoint under `$OPENFOLD_CACHE` automatically. Only pass `--inference-ckpt-path <file.pt>` if you have a non-standard layout or multiple checkpoints and need to pin one explicitly.

For MSA + templates (slower, higher accuracy), drop the two `false` flags. The MSA server is `api.colabfold.com`; template chain-ID remap hits `data.rcsb.org` (GraphQL) — both must be reachable.

Query JSON format

OpenFold3 does **not** read FASTA. Queries are a JSON object validated by `InferenceQuerySet` (pydantic, `extra: forbid` — unknown keys reject):

{
  "queries": {
    "my_complex": {
      "chains": [
        { "molecule_type": "protein", "chain_ids": ["A"], "sequence": "MQIFVK…" },
        { "molecule_type": "protein", "chain_ids": ["B", "C"], "sequence": "MVLSPA…" },
        { "molecule_type": "ligand", "chain_ids": ["L"], "smiles": "CC(=O)Oc1ccccc1C(=O)O" }
      ],
      "use_msas": true
    }
  },
  "seeds": [42]
}

| `molecule_type` | required field | | ------------------------- | ------------------------------------ | | `protein` / `dna` / `rna` | `sequence` | | `ligand` | `smiles` **or** `ccd_codes: ["HEM"]` |

`chain_ids` is a list — repeat the same sequence across multiple chain IDs for homo-oligomers. Per-chain `paired_msa_file_paths` / `main_msa_file_paths` let you supply your own a3m instead of the server.

Key parameters

| Flag | Default | Description | | ------------------------- | --------------------------------------- | ------------------------------------------------------------------------------------------------------ | | `--num-diffusion-samples` | 5 | Structures per (query, seed) | | `--num-model-seeds` | 1 | Number of model seeds per query (multiplies output count alongside JSON `seeds` and diffusion samples) | | `--use-msa-server` | true | ColabFold MMseqs2 server for MSA | | `--use-templates` | true | ColabFold template search + RCSB remap | | `--inference-ckpt-path` | auto-discovered under `$OPENFOLD_CACHE` | Override only — for non-standard layouts or to pin a specific checkpoint file |

Output format

out/
├── summary.txt
├── model_config.json / experiment_config.json
├── inference_query_set.json
└── <query_name>/seed_<N>/
    ├── <query>_seed_<N>_sample_<k>_model.cif
    ├── <query>_seed_<N>_sample_<k>_confidences.json           # full PAE/pLDDT
    ├── <query>_seed_<N>_sample_<k>_confidences_aggregated.json
    └── timing.json

`*_confidences_aggregated.json` is the small one to read first:

{
  "avg_plddt": 78.96,