microsoft / bioemu2

# BioEmu2
### A foundation model for biomolecular structure and dynamics
**Predict folded structures, sample equilibrium ensembles, and propagate structures forward in time.**
Installation · Quick start · Tutorials · Documentation
 BioEmu2 simulating the PUMA BH3 peptide binding MCL-1. 
---
BioEmu2, short for "biomolecular emulator", is developed by Microsoft Research
AI for Science.
Key features include:
- molecular systems containing proteins, nucleic acids, ligands, and other
 chemical components;
- equilibrium sampling and structure prediction from the command line or
 Python;
- time-lagged trajectory generation with implicit transfer operator models;
- contact conditioning and inference-time steering toward desired properties;
- context-parallel sampling of structures that do not fit on one GPU.
## System requirements
BioEmu2 requires Linux and an NVIDIA GPU; macOS and Windows are not supported.
The `cuda` extra installs CUDA 13 cuEquivariance, which requires compute
capability 7.5 or newer, such as NVIDIA T4, A100, RTX A6000, L40S, H100, and B200
(CUDA 13 release notes).
GPUs with compute capability 7.0 (Volta, such as NVIDIA V100) need the CUDA 12
installation instead.
We have tested V100 (compute capability 7.0), T4 (7.5, with the CUDA 12
installation), A100 and RTX A6000 (8.0 and 8.6), and B200 (10.0); other eligible
GPUs have not been verified end to end. Check
your GPU's compute capability with
`nvidia-smi --query-gpu=name,compute_cap --format=csv`.
Context-parallel sampling requires at least four GPUs.
## Installation
Use Python 3.12, 3.13, or 3.14 on a supported Linux machine. We recommend
installing BioEmu2 in an isolated
virtual environment or
conda environment.
To install in an isolated virtual environment, create and activate it from the
repository root:
 [code] 
Replace `python3.12` with the name or path of your Python 3.12, 3.13, or 3.14
executable if needed. Use `python -m pip` so packages are installed into the
active Python environment rather than a different interpreter's `pip`.
Run the installation command for your GPU from the repository root.
For GPUs with compute capability 7.5 or newer, such as NVIDIA T4, A100,
RTX A6000, L40S, H100, and B200, use the `cuda` extra:
 [code] 
Compute capability 7.0 (Volta, such as NVIDIA V100) is the oldest supported;
on these GPUs, use the `cuda12` extra with the PyTorch CUDA 12.6 package index:
 [code] 
GPUs with compute capability below 7.0 are not supported.
We tested BioEmu2 and evaluated its benchmark metrics with Python 3.12. Python
3.13 and 3.14 pass the test suite, but we have not evaluated benchmark metrics
with them.
For development, see DEVELOPER.md.
For Jupyter or VS Code notebooks, follow the notebook
quickstart to install visualization dependencies and
register a kernel.
## Quick start
### CLI
Sample eight equilibrium conformations of adenylate kinase (PDB 1AKE):
 [code] 
Or predict a folded structure from a PDB entry:
 [code] 
Propagate each sampled adenylate kinase confo