The google-deepmind/alphafold3 repository provides the inference pipeline for AlphaFold 3, a cutting-edge system designed for predicting the three-dimensional structures of biomolecules, including proteins and their interactions. This repository contains the necessary code to run the AlphaFold 3 inference process, enabling users to generate structural predictions based on input sequences. The primary purpose of this repository is to facilitate the use of AlphaFold 3 for research and exploration of biomolecular structures.

The core functionality of the repository revolves around the inference pipeline. This pipeline takes input data, such as amino acid sequences, and processes it through a series of computational steps to generate a predicted 3D structure. The repository includes the `run_alphafold.py` script, which serves as the main entry point for running the inference. Users can configure the pipeline using command-line flags to control various aspects of the prediction process, including whether to run the data pipeline (genetic and template search) and the inference itself. The data pipeline is CPU-intensive and handles tasks like searching for homologous sequences and identifying potential templates, while the inference stage leverages GPUs for the computationally demanding structure prediction.

A key aspect of using this repository is obtaining the model parameters. These parameters are essential for the AlphaFold 3 model to function correctly. Access to these parameters is granted at the sole discretion of Google DeepMind, and users must request access through a provided form. The use of the model parameters is governed by specific terms of use, emphasizing the importance of adhering to the licensing and usage guidelines.

The repository provides detailed documentation to guide users through the installation and usage of AlphaFold 3. This includes instructions for setting up the environment, preparing input data, and running the prediction pipeline. Example input files, such as the `fold_input.json` file, are provided to help users understand the required input format. The documentation also covers the expected output format and provides guidance on interpreting the results. Furthermore, the repository includes documentation on performance characteristics and known issues, enabling users to understand the limitations and potential challenges associated with the model.

The repository is designed to be used with Docker, which simplifies the installation and execution process. Users can run the AlphaFold 3 pipeline within a Docker container, ensuring a consistent and reproducible environment. The provided Docker command demonstrates how to mount input and output directories, as well as the directory containing the model parameters and databases.

The repository also emphasizes the importance of proper citation. Users are required to cite the relevant publication, "Accurate structure prediction of biomolecular interactions with AlphaFold 3," in any publications that utilize the source code, model parameters, or outputs generated by the system. This ensures that the contributions of the AlphaFold 3 development team are properly acknowledged.

Finally, the repository includes information on licensing, disclaimers, and acknowledgements. The source code is licensed under a Creative Commons Attribution-Non-Commercial ShareAlike International License, while the model parameters are subject to the AlphaFold 3 Model Parameters Terms of Use. The repository also clarifies that it is not an officially supported Google product and provides contact information for the AlphaFold team for any questions or feedback. The repository also lists the third-party software and databases used by AlphaFold 3, along with their respective licenses.