Technical Report of HelixFold3 for Biomolecular Structure Prediction
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916537015730176 |
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| author | Liu, Lihang Zhang, Shanzhuo Xue, Yang Ye, Xianbin Zhu, Kunrui Li, Yuxin Liu, Yang Gao, Jie Zhao, Wenlai Yu, Hongkun Wu, Zhihua Zhang, Xiaonan Fang, Xiaomin |
| author_facet | Liu, Lihang Zhang, Shanzhuo Xue, Yang Ye, Xianbin Zhu, Kunrui Li, Yuxin Liu, Yang Gao, Jie Zhao, Wenlai Yu, Hongkun Wu, Zhihua Zhang, Xiaonan Fang, Xiaomin |
| contents | The AlphaFold series has transformed protein structure prediction with remarkable accuracy, often matching experimental methods. AlphaFold2, AlphaFold-Multimer, and the latest AlphaFold3 represent significant strides in predicting single protein chains, protein complexes, and biomolecular structures. While AlphaFold2 and AlphaFold-Multimer are open-sourced, facilitating rapid and reliable predictions, AlphaFold3 remains partially accessible through a limited online server and has not been open-sourced, restricting further development. To address these challenges, the PaddleHelix team is developing HelixFold3, aiming to replicate AlphaFold3's capabilities. Leveraging insights from previous models and extensive datasets, HelixFold3 achieves accuracy comparable to AlphaFold3 in predicting the structures of the conventional ligands, nucleic acids, and proteins. The initial release of HelixFold3 is available as open source on GitHub for academic research, promising to advance biomolecular research and accelerate discoveries. The latest version will be continuously updated on the HelixFold3 web server, providing both interactive visualization and API access. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Technical Report of HelixFold3 for Biomolecular Structure Prediction Liu, Lihang Zhang, Shanzhuo Xue, Yang Ye, Xianbin Zhu, Kunrui Li, Yuxin Liu, Yang Gao, Jie Zhao, Wenlai Yu, Hongkun Wu, Zhihua Zhang, Xiaonan Fang, Xiaomin Biomolecules Artificial Intelligence Machine Learning The AlphaFold series has transformed protein structure prediction with remarkable accuracy, often matching experimental methods. AlphaFold2, AlphaFold-Multimer, and the latest AlphaFold3 represent significant strides in predicting single protein chains, protein complexes, and biomolecular structures. While AlphaFold2 and AlphaFold-Multimer are open-sourced, facilitating rapid and reliable predictions, AlphaFold3 remains partially accessible through a limited online server and has not been open-sourced, restricting further development. To address these challenges, the PaddleHelix team is developing HelixFold3, aiming to replicate AlphaFold3's capabilities. Leveraging insights from previous models and extensive datasets, HelixFold3 achieves accuracy comparable to AlphaFold3 in predicting the structures of the conventional ligands, nucleic acids, and proteins. The initial release of HelixFold3 is available as open source on GitHub for academic research, promising to advance biomolecular research and accelerate discoveries. The latest version will be continuously updated on the HelixFold3 web server, providing both interactive visualization and API access. |
| title | Technical Report of HelixFold3 for Biomolecular Structure Prediction |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.16975 |