# bytedance/Protenix

Toward High-Accuracy Open-Source Biomolecular Structure Prediction.

Repository: https://github.com/bytedance/Protenix
Canonical: https://ross.abutalabs.com/products/protenix
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai4science, research
Last push: 2026-08-01T10:53:40+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 95, release rhythm 78, longevity 47
- inputs: {"age_days": 663, "days_push": 32, "days_rel": 148, "gap_med": 5.0, "n_releases_24m": 45}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2038, forks 306 (observed 2026-08-28T04:06:08.202268+00:00)

## What it is
Protenix is an open-source PyTorch reproduction of AlphaFold 3 for high-accuracy biomolecular structure prediction, covering proteins, nucleic acids, and ligands. It is developed by ByteDance as a trainable, extensible research tool for the computational biology community.

## Use cases
- predict 3D structure of a protein complex from sequence
- predict antibody-antigen binding structures
- train a biomolecular structure prediction model in PyTorch
- predict protein-ligand and protein-RNA interactions
- run an open-source alternative to AlphaFold 3
- evaluate structure prediction models with PXMeter benchmarks

## When to choose
- you need an open-source, trainable AlphaFold 3-style model
- you want to fine-tune or extend structure prediction research
- you need antibody-antigen or protein-ligand complex prediction
- you work in computational biology with GPU resources

## When to avoid
- you need a lightweight tool without GPU hardware
- you only need classical molecular docking rather than deep-learning prediction
- you need a polished end-user GUI application rather than a research codebase

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training
- domain: bioinformatics, artificial-intelligence, machine-learning
- platform: python
- tags: protein-structure-prediction, alphafold3, pytorch, computational-biology, ai4science, molecular-docking, linux, gpu

## Member repositories
- bytedance/Protenix (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:08.202268+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 2026-08-30T02:59:14.157954+00:00, confidence not recorded.
  - readme: https://github.com/bytedance/Protenix (fetched 2026-08-28T04:06:08.202268+00:00, sha d301319f3305)
  - registry_pypi: https://pypi.org/pypi/protenix/json (fetched 2026-08-29T10:38:47.113203+00:00, sha ee0b44f46f85)
- Data as of 2026-08-30T08:39:29.467469+00:00.