BioEmu
| Property | Value |
|---|---|
| Parameter Count | 31M |
| License | MIT |
| Paper | Link |
| Training Data Size | 161k structures, 216ms MD simulations, 19k dG measurements |
What is BioEmu?
BioEmu is a groundbreaking deep learning model developed by Microsoft Research that efficiently predicts biomolecular equilibrium structure ensembles. Using the DiG architecture, it can generate thousands of statistically independent protein structures per hour on a single GPU, representing a significant advancement in computational biology.
Implementation Details
The model utilizes a combination of denoising score matching and property prediction fine-tuning (PPFT) approaches. It's trained on a diverse dataset including AlphaFold DB structures, molecular dynamics simulations, and experimental protein stability measurements.
- Architecture: DiG-based design with 31M parameters
- Training approach: Two-phase training with pretraining and fine-tuning
- Performance: Achieves MAE of 0.91 kcal/mol in MD equilibrium distributions
Core Capabilities
- Prediction of structural ensembles with high accuracy
- Sampling of conformational changes including cryptic pockets and domain motions
- Prediction of protein folding free energies
- Generation of mechanistic hypotheses for protein behavior
Frequently Asked Questions
Q: What makes this model unique?
BioEmu stands out for its ability to efficiently sample protein conformational changes while maintaining thermodynamic accuracy, achieving state-of-the-art performance in predicting protein stability with errors around 1 kcal/mol.
Q: What are the recommended use cases?
The model is specifically designed for predicting protein structure ensembles, analyzing conformational changes, and studying protein stability. It's particularly useful for research applications but requires further development for real-world applications.
