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开发机构 Meta AI / ESM contributors →
Protein structure inference from sequence with a protein language model. Memory requirements increase with sequence length; this is the original ESMFold implementation.
Protein structure inference from sequence with a protein language model. Memory requirements increase with sequence length; this is the original ESMFold implementation.
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 ESMFold in scientific publications and benchmark papers.
@software{esmfold_2026,
title = {{ESMFold}},
author = {{Meta AI / ESM contributors}},
year = {2026},
url = {https://github.com/facebookresearch/esm},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for ESMFold.
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
No software–hardware integration has been verified for this entry yet. Explore documented research workflows.
ESMFold Scientific Architecture · Credit: Meta AI / ESM 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
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AI system for generating novel, high-affinity protein binders directly from target structures
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暂无与此工具关联的已验证学术动态。
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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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