openfold3 skill | PlugMCP

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1Install this skill: https://github.com/aipoch/open-science/tree/HEAD/resources/skills/openfold3

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resources/skills/openfold3/SKILL.mdMarkdown · UTF-8

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File: resources/skills/openfold3/SKILL.md
Publisher: aipoch
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From the publisher

SKILL.md

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

{
  "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]
}

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

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

{
  "avg_plddt": 78.96,
  "ptm": 0.667,
  "iptm": 0.0,
  "gpde": 0.73,
  "has_clash": 0.0,
  "sample_ranking_score": 0.133,
  "chain_ptm": { "A": 0.667 },
  "chain_pair_iptm": {}
}

grep -E 'Successful|Failed' out/summary.txt
find out -name '*_model.cif' | wc -l   # = queries x json_seeds x num-model-seeds x num-diffusion-samples

Error	Cause	Fix
`_deepspeed_evo_attn requires that DeepSpeed be installed`	default eval kernel is DS4Sci on CUDA	install `deepspeed` (needs nvcc + CUTLASS), or in `model_config.py` eval block set `use_deepspeed_evo_attention: False` + `use_cueq_triangle_kernels: True` (cuEq path; no build)
`CUTLASS_PATH ... not set ... cutlass_library is not installed`	cuEq path still needs the python `cutlass_library` shim	`pip install nvidia-cutlass`
`libXrender.so.1: cannot open shared object file`	rdkit (via pdbeccdutils) needs X11 render libs	`apt-get install libxrender1 libxext6 libsm6`
`ModuleNotFoundError: boto3` (or `awscrt`)	`openfold3.core.data.io.s3` is eager-imported even when weights are local	`pip install boto3 awscrt`
`ValidationError: queries / Field required` or `Input should be an object`	wrong JSON shape	top-level is `{"queries": {"<name>": {...}}}` (a dict, not a list)
`ValidationError ... settings / Extra inputs are not permitted`	tried to override model config via `--runner-yaml`	`--runner-yaml` is `InferenceExperimentConfig` only; kernel/memory settings live in `model_config.py`
`Failed to fetch chain ID mappings from RCSB for N entries`	`data.rcsb.org` unreachable (allowlist/offline)	run with `--use-templates false`, or open egress to `data.rcsb.org`
`CUDA out of memory`	large complex / many samples	reduce `--num-diffusion-samples`; the `low_mem` preset (`model_setting_presets.yml`) offloads more aggressively

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

openfold3

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Skill facts

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SKILL.md

OpenFold3 Structure Prediction

Prerequisites

How to run

Installation

Weights

Running

Query JSON format

Key parameters

Output format

What good output looks like

Verify

Troubleshooting