PySTARC computes bimolecular association rate constants (kon) via GPU-accelerated rigid-body Brownian dynamics.
- GPU batch simulation - All trajectories run simultaneously on GPU via CuPy.
- Physics - Ermak-McCammon integrator, RPY hydrodynamics, Born desolvation, APBS electrostatics, adaptive time step, and Yukawa monopole fallback.
- Brownian bridge - Catches mid-step reaction crossings.
- Multi-GPU workflow - Split simulations across N GPUs with automatic grid generation, symlinked DX files, and pooled result combining.
- Automated system setup - From a PDB and topology file to a ready-to-run simulation in one command via
setup.py. - Convergence analysis - Wilson score CI, relative SE, and trajectory-count estimates for target precision.
- Output files - 14 structured files, including trajectories, encounters, radial density, angular maps, and transition matrices.
- Checkpointing - Automatic save/resume for long production runs.
- Live progress - kon and Prxn printed at configurable intervals.
- Temperature scaling - Correct thermodynamics at any temperature.
GPU (Linux/HPC):
git clone https://github.com/anandojha/PySTARC.git
cd PySTARC
bash install_PySTARC.shOn Mac/CPU:
git clone https://github.com/anandojha/PySTARC.git
cd PySTARC
conda create -n PySTARC python=3.11 -y
conda activate PySTARC
conda install -c conda-forge ambertools apbs -y
pip install matplotlib pdb2pqr
pip install dist/pystarc-1.1.0-py3-none-any.whl --force-reinstallpython -m pytest tests/ -v conda activate PySTARC
module load cuda # HPC only, skip on local machines
cd examples/two_charged_spheres
chmod +x run.sh
bash run.shSee examples/README.md for complete instructions.
MIT
When using PySTARC, please cite:
Ojha, A. A. et al. PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants (2026).
- Python 3.11+
- AmberTools (tleap, cpptraj, ambpdb)
- APBS
- CuPy (GPU) or NumPy (CPU fallback)
- NVIDIA GPU with CUDA 12+ (recommended)
