All-atom biomolecular structure prediction for proteins, nucleic acids, and small molecules
HelixFold 3 is a comprehensive biomolecular structure prediction platform built on PaddlePaddle, predicting 3D coordinates for complexes of proteins, DNA, RNA, ligands, and covalent modifications.
Unified prediction of protein-ligand, protein-nucleic acid, and multi-chain assembliesHigh-throughput distributed inference accelerated by PaddleHelix engineIncludes specialized chemical bond loss for precise covalent drug interactionsOpenly accessible web server and downloadable weights for enterprise deployment
HelixFold 3 is a comprehensive biomolecular structure prediction platform built on PaddlePaddle, predicting 3D coordinates for complexes of proteins, DNA, RNA, ligands, and covalent modifications.
Informations confrontées à une source officielle ; pas un banc d’essai expérimental. Source · Dernière vérification : 20/09/2026, 11:09:40 UTC
Fonctionnalités clés
Unified prediction of protein-ligand, protein-nucleic acid, and multi-chain assemblies
High-throughput distributed inference accelerated by PaddleHelix engine
Includes specialized chemical bond loss for precise covalent drug interactions
Openly accessible web server and downloadable weights for enterprise deployment
Interactive 3D Illustration
Biological Architecture (Stylized Ribbon)
Default view is a stylized demonstration ribbon. Enter any 4-letter RCSB PDB accession code above to stream real atomic coordinates.
⇄ Drag to rotate · Scroll to zoom
Illustrative Confidence Gradient:
>90 High / Low B 70-90 Confident 50-70 Low <50 Very low / High B
Academic Context & Research Evidence
Biological & Workflow Fit
Biological Application
Target-ligand interaction modeling, RNA-protein complex prediction, and covalent drug discovery
Research Workflow
Input biomolecular sequence and SMILES -> generate 3D complex coordinates and confidence scores
Use this citation format when referencing HelixFold 3 in scientific publications and benchmark papers.
@software{helixfold_3_2026,
title = {{HelixFold 3}},
author = {{Baidu PaddleHelix}},
year = {2026},
url = {https://github.com/PaddlePaddle/PaddleHelix},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}
Peer-Reviewed Literature & Preprints
Live scientific citations queried from Europe PMC and PubMed for HelixFold 3.
⏳ Querying Europe PMC & PubMed for indexed publications...
Informations techniques et produit
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
Entry typeAI Model / Web Server
Access modeOpen Source & Web Service
AI roleStructural Complex Modeling
Input dataNot recorded
Output dataNot recorded
Licence conditionsApache 2.0 (code) / Model License
Commercial eligibilityOpen research access with enterprise private deployment
Compute requirements1x NVIDIA A100 or Kunlunxin AI accelerators
ValidationNot recorded
TypeAll-atom structure prediction model
Intended useNot recorded
CompatibilityNot recorded
ManufacturerBaidu Inc.
Biological applicationTarget-ligand interaction modeling, RNA-protein complex prediction, and covalent drug discovery
Research workflowInput biomolecular sequence and SMILES -> generate 3D complex coordinates and confidence scores
Evidence levelPreprint and blind benchmark evaluation (2024)