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ALPHAFOLD3 Pytorch

A public Python/PyTorch implementation of AlphaFold 3 for computational protein and other biomolecular structure prediction.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

ALPHAFOLD3 Pytorch — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when the work requires a code-based, self-managed PyTorch implementation for research or experimentation with AlphaFold 3-style biomolecular structure prediction.

Who it is for

  • Researchers studying biomolecular structure prediction.
  • Python and PyTorch developers experimenting with AlphaFold 3 architectures.
  • Computational biology users building local or self-hosted research workflows.

Documented uses

  • Install the implementation as a Python package.
  • Construct the AlphaFold 3 model in PyTorch.
  • Experiment with transformer, attention and denoising-diffusion approaches.
  • Prepare research workflows involving molecule, atom, template and MSA inputs.
Before you choose

Requirements & limits

The repository is public and identified with an MIT license.

  • It is a library rather than a documented hosted prediction service.
  • The implementation is Python/PyTorch based and uses structured atom, molecule, template and MSA inputs.
  • The README distinguishes training inputs from inference use by noting that certain labels are required for training but omitted on inference.
  • Related resources include a Lightning and Hydra fork, an interactive visualization notebook and a Triton-kernel optimized implementation.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the ALPHAFOLD3 Pytorch package

Set up the documented Python package so the library can be imported in a PyTorch workflow.

Before you start

  • Python and a PyTorch environment, as shown by the repository's Python/PyTorch usage.
  • Access to the public GitHub repository.

Steps

  1. Run the documented package installation command: the command below.

    Documented command
    pip install alphafold3-pytorch

  2. Import torch and Alphafold3 from alphafold3_pytorch as shown in the README.

  3. Construct an Alphafold3 instance with the documented example dimensions, such as dim_atom_inputs = 77 and dim_template_feats = 108.

Expected outcome

The package is installed and the documented model-construction stage is prepared for a PyTorch workflow.

Guide limitations

  • The supplied documentation does not establish hardware requirements, complete environment specifications, or a verified prediction result.
  • The README example uses mock inputs and does not document a complete end-to-end deployment procedure.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
30 September 2026
Original website title
lucidrains/alphafold3-pytorch · Official homepage title · retained in its source language
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