DEDUPEDWhat do you want to do?
3 use cases
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Predict a biomolecular complex from a JSON input
Write a fold_input.json for your sequences and run the container to get a predicted structure.
View 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.
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Run the CPU search and the GPU fold on different machines
Use the run flags to do genetic and template search on a CPU box and inference on a GPU box.
View prompt
With google-deepmind/alphafold3, show me how to run the data pipeline on my CPU server and inference on my GPU server using --run_data_pipeline and --run_inference, and what to copy between them.
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Pick the right AlphaFold 3 weights for your project
Compare the standard weights with the SynthID Bio-structure and Leaving Atom variants before you run.
View prompt
Using google-deepmind/alphafold3, compare the standard, SynthID Bio-structure and Leaving Atom weights plus the server and Google Cloud routes for <my project>, and tell me which fits.
Quick start with Alphafold3
- Check you have what it needs: 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.
- Open google-deepmind/alphafold3 on GitHub and read the README and license.
- 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.pywith --json_path, --model_dir and --output_dir - Read docs/output.md to interpret the files you get back
These steps come from our top use case, Predict a biomolecular complex from a JSON input, which also has a copy-paste agent prompt.
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Get them freeAbout this repo
- Who it is forStructural biology groups with GPU access who want to run AlphaFold 3 predictions themselves.
- Why it is hot8.7k stars, picked up by one AI source we track
- Sourcegithub.com/google-deepmind/alphafold3 ยท Back to Hot repos
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
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Questions about Alphafold3
What is Alphafold3?
AlphaFold 3 is Google DeepMind's inference pipeline for predicting the structure of biomolecular complexes. You describe the complex in a JSON input file and run run_alphafold.py, usually inside the provided container, with separate flags for the CPU heavy data pipeline and the GPU inference step. Model parameters are not in the repo: you must request them from Google and follow their terms of use. It is an open-source project on GitHub: https://github.com/google-deepmind/alphafold3.
How do I use Alphafold3?
Follow the installation documentation in docs/installation.md and obtain the model parameters from Google; then write a fold_input.json with name, sequences, modelSeeds, dialect Alphafold3 and version 1, as in the README example. The use cases on this page give the full steps and a copy-paste agent prompt for each job.
Who is Alphafold3 for?
Structural biology groups with GPU access who want to run AlphaFold 3 predictions themselves.
What can I do with Alphafold3?
3 use cases so far, for example: Predict a biomolecular complex from a JSON input; Run the CPU search and the GPU fold on different machines; Pick the right AlphaFold 3 weights for your project. Each has steps and a copy-paste agent prompt.
What do I need to run Alphafold3?
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.
What are alternatives to Alphafold3?
Other hot AI repos in Security and science: Security Audit Skill, GPU Unlock, Syft, Esm, Osv Scanner.
Is Alphafold3 safe to run?
Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.