{
 "schema_version": 1,
 "generated": "2026-10-31",
 "id": "google-deepmind-alphafold3--predict-a-complex-structure-with-alphafold-3",
 "repo": "google-deepmind/alphafold3",
 "repo_url": "https://github.com/google-deepmind/alphafold3",
 "repo_slug": "google-deepmind-alphafold3",
 "title": "Predict a biomolecular complex from a JSON input",
 "page_title": "How to predict a biomolecular complex from a JSON input with Alphafold3",
 "summary": "Write a fold_input.json for your sequences and run the container to get a predicted structure.",
 "who": "A structural biologist who needs a prediction for a specific complex.",
 "problem": "Public servers limit ligands, modifications and throughput.",
 "outcome": "A predicted structure written to your output directory.",
 "steps": [
  "Follow the installation documentation in docs/installation.md and obtain the model parameters from Google",
  "Write a fold_input.json with name, sequences, modelSeeds, dialect Alphafold3 and version 1, as in the README example",
  "Run the docker command in the README with volumes for af_input, af_output, the model parameters and the public databases, calling `python run_alphafold.py` with --json_path, --model_dir and --output_dir",
  "Read docs/output.md to interpret the files you get back"
 ],
 "agent_prompt": "Using google-deepmind/alphafold3, write a fold_input.json for <my sequences>, run the documented docker command with my model and database directories, and explain the output files.",
 "agents": [
  "claude-code",
  "codex",
  "cursor",
  "gemini-cli"
 ],
 "difficulty": "Advanced",
 "time": "4 hours",
 "basis": "readme",
 "caveat": "The repo contains the inference pipeline only; it does not include the model parameters.",
 "category": "Security and science",
 "tags": [
  "Security and science"
 ],
 "lang": "",
 "requirements": [
  "The model parameters, which you may only use if received directly from Google, under the weights terms of use",
  "Docker and an NVIDIA GPU for the inference step",
  "The genetic and template databases for the data pipeline, mounted into the container",
  "A fold_input.json describing your sequences, model seeds, dialect and version"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "credentials",
   "notice": "Model parameters may only be used if received directly from Google and are subject to their terms of use."
  }
 ],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/google-deepmind-alphafold3/predict-a-complex-structure-with-alphafold-3/",
 "md": "https://dedupedai.com/use-cases/google-deepmind-alphafold3/predict-a-complex-structure-with-alphafold-3/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/google-deepmind-alphafold3--predict-a-complex-structure-with-alphafold-3.json",
 "api_version": "v1"
}