# Technical Report of HelixFold3 for Biomolecular Structure Prediction

arXiv: https://arxiv.org/abs/2408.16975 · HTML: https://mlsanity.com/paper/2408.16975/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 30 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [HelixFold-Single: MSA-free Protein Structure Prediction by Using Protein Language Model as an Alternative](https://mlsanity.com/paper/2207.13921/) (2022)
- [HelixFold: An Efficient Implementation of AlphaFold2 using PaddlePaddle](https://mlsanity.com/paper/2207.05477/) (2022)
- [Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models](https://mlsanity.com/paper/2310.13913/) (2023)
- [HelixFold-Multimer: Elevating Protein Complex Structure Prediction to New Heights](https://mlsanity.com/paper/2404.10260/) (2024)

## Built on by (most cited first, 6 of 6)

- [Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches](https://mlsanity.com/paper/2410.00709/) (2024)
- [Pearl: A Foundation Model for Placing Every Atom in the Right Location](https://mlsanity.com/paper/2510.24670/) (2025)
- [HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold3](https://mlsanity.com/paper/2505.21873/) (2025)
- [HelixDesign-Antibody: A Scalable Production-Grade Platform for Antibody Design Built on HelixFold3](https://mlsanity.com/paper/2507.02345/) (2025)
- [End-to-End Reverse Screening Identifies Protein Targets of Small Molecules Using HelixFold3](https://mlsanity.com/paper/2601.13693/) (2026)
- [Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling](https://mlsanity.com/paper/2608.16094/) (2026)

## Abstract (arXiv)

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.