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开发机构 AlQuraishi Laboratory / OpenFold contributors →
Trainable PyTorch implementation of the AlphaFold 2 approach. Training and inference have different resource needs; code and downloaded parameters have separate terms.
Trainable PyTorch implementation of the AlphaFold 2 approach. Training and inference have different resource needs; code and downloaded parameters have separate terms.
信息已比对权威文献或官方白皮书；非实验性跑分。 权威出处 · 最后核对: 16/09/2026, 17:33:40 UTC
Use this citation format when referencing OpenFold in scientific publications and benchmark papers.
@software{openfold_2026,
title = {{OpenFold}},
author = {{AlQuraishi Laboratory / OpenFold contributors}},
year = {2026},
url = {https://github.com/aqlaboratory/openfold},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for OpenFold.
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OpenFold Scientific Architecture · Credit: AlQuraishi Laboratory / OpenFold contributors · Open original ↗
Research and editorial use Source & attribution ↗ License ↗
同一科学领域的同类算法与工具。
Research AI model / method · Research code or publication
All-atom protein structure diffusion conditioned on specific ligand or nucleic acid atomic context.
Baker Lab
AI Model · Research Preview
AI system for generating novel, high-affinity protein binders directly from target structures
Google DeepMind
OpenFold Consortium Publishes Trainable PyTorch AlphaFold2 and Complex Benchmarks
1 Apr 2026
See project access and maintenance status
费用根据官方最近一次公布的说明整理。请在官方网站确认最新报价与学术折扣。
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