Bibliographic Details
| Title: |
Technical Report of HelixFold3 for Biomolecular Structure Prediction |
| Authors: |
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 |
| Publication Year: |
2024 |
| Collection: |
Computer Science
Quantitative Biology |
| Subject Terms: |
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2408.16975 |
| Accession Number: |
edsarx.2408.16975 |
| Database: |
arXiv |
