PaddlePaddle/PaddleHelix
Bio-Computing Platform Featuring Large-Scale Representation Learning and Multi-Task Deep Learning “螺旋桨”生物计算工具集
This platform helps biologists, biochemists, and pharmaceutical researchers predict the complex 3D structures of biomolecules like proteins, nucleic acids, and small ligands. You input the primary sequences of these molecules, and it outputs highly accurate predicted 3D structures. This is crucial for understanding how molecules function and for accelerating drug discovery and development.
1,107 stars. No commits in the last 6 months.
Use this if you need to accurately predict the three-dimensional structure of proteins, protein complexes, nucleic acids, or small molecules for research or drug design, especially when experimental methods are too slow or costly.
Not ideal if you are looking for experimental validation or physical measurement of molecular structures, as this tool provides computational predictions rather than empirical data.
Stars
1,107
Forks
229
Language
Python
License
—
Category
Last pushed
Aug 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/PaddlePaddle/PaddleHelix"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
mxfold/mxfold2
MXfold2: RNA secondary structure prediction using deep learning with thermodynamic integration
kyegomez/Open-AF3
Implementation of Alpha Fold 3 from the paper: "Accurate structure prediction of biomolecular...
mjendrusch/salad
protein structure generation with sparse all-atom denoising models
dptech-corp/Uni-Fold
An open-source platform for developing protein models beyond AlphaFold.
dptech-corp/Uni-Fold-jax
Trainable AlphaFold implementation in JAX