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Python Simulation Toolkit for Association Rate Constants

GPU-accelerated rigid body and flexible chain Brownian dynamics simulations for bimolecular association rate constants

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Overview

PySTARC computes the bimolecular association rate constants by implementing rigid-body Brownian dynamics within the Northrup-Allison-McCammon formalism. Trajectories run in parallel on the GPU with a NumPy CPU fallback.

Installation

GPU (Linux / HPC):

git clone https://github.com/anandojha/PySTARC.git
cd PySTARC
bash install_PySTARC.sh

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 rdkit openbabel -y
conda install -c openeye openeye-toolkits -y
pip install matplotlib pdb2pqr
pip install dist/pystarc-1.1.0-py3-none-any.whl --force-reinstall

Testing

python -m pytest tests/
python -m pytest tests/ -v

Quick start

conda activate PySTARC
module load cuda                # HPC only
cd examples/two_charged_spheres
bash run.sh

Examples

See examples/ for all example systems, each with its own README.

examples/
├── two_charged_spheres/              Analytical validation for the exact Smoluchowski solution
├── trypsin_benzamidine/              Protein-ligand complex
├── beta_cyclodextrin_guests/         Host-guest complex
├── thrombin_thrombomodulin/          Protein-protein complex
├── p38_mapk_sb203580/                Protein-ligand complex
├── carbonic_anhydrase_inhibitors/    Protein-ligand complexes
├── hsp90_inhibitors/                 Protein-ligand complexes
├── ttk_inhibitors/                   Protein-ligand complexes
└── barnase_barstar/                  Protein-protein complex

Requirements

Python 3.11+
AmberTools
APBS
OpenBabel
RDKit
OpenEye Toolkits
NumPy
SciPy
Click
Numba
Matplotlib
pdb2pqr
CuPy

License

MIT

Citation

When using PySTARC, please cite:

Ojha et al. PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants (2026).