# google-deepmind/alphafold

Open source code for AlphaFold 2.

Repository: https://github.com/google-deepmind/alphafold
Canonical: https://ross.abutalabs.com/products/alphafold
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-04-22T17:58:21+00:00

## Health v2 (maintenance only)
Score: 58/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 78, release rhythm 8, longevity 100
- inputs: {"age_days": 1903, "days_push": 133, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 14811, forks 2901 (observed 2026-08-28T04:11:07.736805+00:00)

## What it is
Open-source implementation of the AlphaFold 2 inference pipeline for predicting protein structures from amino acid sequences, including AlphaFold-Multimer for complexes. It is a Python package run via Docker on Linux with NVIDIA GPU support.

## Use cases
- predict the 3D structure of a protein from its sequence
- model protein-protein complexes with AlphaFold-Multimer
- run structure predictions on a GPU machine with genetic databases
- reproduce CASP14/CASP15 baseline predictions
- generate PDB structure files for downstream biology research

## When to choose
- you need high-accuracy protein structure or complex predictions on Linux with an NVIDIA GPU
- you want the official DeepMind implementation rather than community rewrites
- you can afford large genetic database downloads (~3 TB disk)

## When to avoid
- you are on Windows or macOS, which are unsupported
- you lack a modern NVIDIA GPU or the required disk space
- you need training/fine-tuning of the model, since only inference is provided
- you want a lightweight tool without heavy database dependencies

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-inference
- domain: bioinformatics, deep-learning, machine-learning
- platform: python
- tags: protein-structure-prediction, alphafold, computational-biology, structural-biology, inference-pipeline, linux, docker, gpu

## Member repositories
- google-deepmind/alphafold (main) score 58

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:07.736805+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:07:08.790816+00:00, confidence not recorded.
  - readme: https://github.com/google-deepmind/alphafold (fetched 2026-08-28T04:11:07.736805+00:00, sha 18a6258c823f)
- Data as of 2026-08-30T08:39:29.467469+00:00.