Abstract

In this technical report, we present Protenix, a comprehensive reproduction of AlphaFold3 (AF3), aimed at advancing the field of biomolecular structure prediction. Protenix tackles the challenges of predicting complex interactions involving proteins, ligands, and nucleic acids, while enhancing accessibility and reproducibility. Across diverse benchmarks, including PoseBusters V2, low-homology PDB sets, and CASP15 RNA, Protenix achieves state-of-the-art performance in protein-ligand, protein-protein, and protein-nucleic acid predictions. We also address limitations, such as potential memorization effects, and outline future directions for improvement. By open-sourcing Protenix, we aim to democratize advanced structure prediction tools and accelerate interdisciplinary research in computational biology and drug discovery.

Competing Interest Statement

The authors have declared no competing interest.

Details

Title
Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction
Author
Chen, Xinshi; Zhang, Yuxuan; Chan, Lu; Ma, Wenzhi; Guan, Jiaqi; Gong, Chengyue; Yang, Jincai; Zhang, Hanyu; Zhang, Ke; Wu, Shenghao; Zhou, Kuangqi; Yang, Yanping; Liu, Zhenyu; Wang, Lan; Shi, Bo; Shi, Shaochen; Xiao, Wenzhi
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2025
Publication date
Jan 11, 2025
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
3154281122
Copyright
© 2025. This article is published under http://creativecommons.org/licenses/by/4.0/ (“the License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.