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structure-prediction

structure-prediction

Plan and validate PaddleHelix HelixFold structure-prediction workflows without unsafe downloads or GPU inference.

Independent GitHub snapshot · Automated classification · Original documentation governs

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name: structure-prediction description: "Plan and validate PaddleHelix HelixFold structure-prediction workflows without unsafe downloads or GPU inference." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Structure Prediction

Use this sub-skill when a request mentions HelixFold3 input JSON, HelixFold inference, MSA or database paths, checkpoint placement, bf16 versus fp32, protein structure output files, or HelixFold-S1 modules.

Safety Boundary

Do not download genetic databases or model weights, launch training, launch GPU/DCU inference, mutate a user environment, or invoke original repository launch scripts as a default action. If the user explicitly approves execution, construct a command from the bundled references after validating local paths and runtime prerequisites. Reduced HelixFold3/S1 databases are hundreds of GB after extraction, and the inference commands assume a compatible PaddlePaddle GPU stack plus MSA binaries.

Safe default actions:

  1. Classify the requested workflow: HelixFold3, HelixFold-S1, HelixFold, or HelixFold-Single.
  2. Validate or review inputs using scripts/validate_helixfold3_input.py before planning any run.
  3. Check that required paths are user-provided: MSA binaries, databases, checkpoint files, input JSON/FASTA, and output directory.
  4. Plan command substitutions and resource choices without executing the command.
  5. Explain expected output files and ranking/metric files so the user can inspect an existing run.

The lightweight PaddleHelix inspection environment is not a HelixFold runtime environment. Treat PaddlePaddle GPU, MSA tools, OpenMM/PDBFixer, pgl, RDKit, model checkpoints, and genetic databases as optional workflow dependencies that must be planned and confirmed before use.

Route Map

  • For HelixFold3 biomolecular JSON, command anatomy, precision choices, outputs, and non-commercial caveats, read references/helixfold3.md.
  • For HelixFold, HelixFold-Single, and HelixFold-S1 distinctions, training/inference boundaries, and S1 module flow, read references/helixfold-family.md.
  • For entity JSON, FASTA, database, checkpoint, and output path contracts, read references/data-formats.md.
  • For common failures such as malformed JSON, missing MSA tools, CUDA/Paddle mismatch, unsupported bf16, token/memory limits, or missing checkpoints, read references/troubleshooting.md.
  • For general protein sequence modeling, tokenization, TAPE-style prediction, or protein function workflows, route to ../protein-sequence-function/SKILL.md.
  • For compound docking or HelixDock workflows, route to ../compound-drug-discovery/SKILL.md.

Bundled Validator

Run this only as a local JSON/schema preflight; it does not import Paddle, download data, or start inference:

python scripts/validate_helixfold3_input.py --help
python scripts/validate_helixfold3_input.py input.json --mode helixfold3 --max-tokens 1200
python scripts/validate_helixfold3_input.py input.json --mode helixfold-s1

Use --mode helixfold-s1 when validating the S1 top-level fields (job_name, recycle, ensemble, model_type), s1_sample_constraint, S1 sidechain replacements, and the S1 multi-chain expectation. Use --strict-sequence-alphabet only when the user wants alphabet-level rejection rather than warnings.

Planning Checklist

  • Confirm commercial/non-commercial constraints before using HelixFold3 model code, model parameters, or the free server output.
  • Prefer fp32 on V100-class GPUs; use bf16 only on hardware known to support it, such as A100/H100-class GPUs.
  • Keep infer_times, diffusion batch size, token count, and subbatch/recycle settings conservative when planning for 32 GB GPUs.
  • Treat full_dbs support for HelixFold3/S1 as unavailable unless the user has current evidence that their local version supports it; documented reduced databases are already large.
  • Never assume the validation script proves that checkpoints, MSA tools, databases, CUDA, Paddle, or GPU memory are available. It only proves the input file shape is plausible.
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