https://github.com/ci-lab-cz/streamd
Fully automated high-throughput MD pipeline
https://github.com/ci-lab-cz/streamd
gromacs gromacs-tools molecular-dynamics molecular-dynamics-simulation
Last synced: 5 months ago
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Fully automated high-throughput MD pipeline
- Host: GitHub
- URL: https://github.com/ci-lab-cz/streamd
- Owner: ci-lab-cz
- License: mit
- Created: 2022-02-23T11:32:07.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-12-27T20:52:18.000Z (6 months ago)
- Last Synced: 2025-12-28T11:58:22.133Z (6 months ago)
- Topics: gromacs, gromacs-tools, molecular-dynamics, molecular-dynamics-simulation
- Language: Python
- Homepage:
- Size: 6.53 MB
- Stars: 86
- Watchers: 4
- Forks: 16
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- License: LICENSE.txt
Awesome Lists containing this project
- awesome-ai4molconformation-md - StreaMD - A tool to perform high-throughput automated molecular dynamics simulations. (Molecular dynamics / MD Engines-Frameworks)
README
# StreaMD: a tool to perform high-throughput automated molecular dynamics simulations
StreaMD provides an end-to-end molecular dynamics workflow that takes a PDB structure as input and automatically performs system preparation, equilibration, production and continuation runs, and analysis,
producing XTC trajectories together with ready-to-use plots and CSV outputs.
## Features:
- Run multiple simultaneous molecular dynamics simulations
- Run multiple replicas of the same system for multiple complexes in a single command
- Simulation for different systems:
- Protein in Water;
- Protein - Ligand;
- Protein - Cofactor (multiple);
- Protein - Ligand - Cofactor (multiple);
- Simulations of boron-containing molecules using Gaussian software
- Simulations of ligand-binding metalloproteins with MCPB.py
- Distributed computing using dask library
- Running parallel simulations on multiple servers
- Extending the time of MD simulations
- Continuing interrupted MD simulations
- Restarting interrupted MD preparation by invoking the same command
- Implemented tools for end-state free energy calculations (gmx_MMPBSA) and protein–ligand interaction analysis (ProLIF)
- Support for customized .mdp files
- Interactive trajectory convergence analysis for multiple complexes
- GPU support
## Quick start
```bash
# Create environment (choose CPU-only or GPU)
conda env create --file env.yml -n md # or env_gpu.yml on GPU-capable hosts
conda activate md
# Install
pip install streamd
# or latest main branch
pip install git+https://github.com/ci-lab-cz/streamd.git
```
**Minimal protein-ligand run (1 ns)**
```
run_md -p protein.pdb -l ligand.mol --md_time 1
```
**Protein - multiple ligands multiple replicas runs (1 ns)**
```
run_md -p protein.pdb -l ligands.sdf --md_time 1 --replicas 3 --seed 1024
```
**Extend successfully finished simulations**
```
run_md --wdir_to_continue md_files/md_run/protein_H_HIS_ligand_*/ --md_time 10
```
**GPU-accelerated simulations**
```
run_md -p protein_HIS.pdb -l ligand.mol --md_time 1 --device gpu --ncpu 32
```
More examples can be found in the [documentation](https://streamd.readthedocs.io/)
## Documentation
https://streamd.readthedocs.io/
## License
MIT
## Ready-to-use containers (Apptainer)
Pre-built `.sif` images are available (CPU and GPU) in the [Zenodo record](https://zenodo.org/records/18176058)
**CPU:** `apptainer run --cleanenv streamd_cpu.sif run_md --help`
**GPU:** `apptainer run --nv --cleanenv streamd_gpu.sif run_md --help`
The provided `.sif` images are intended for Apptainer on Linux/HPC systems.
GPU usage requires an NVIDIA GPU node and launching with `--nv` and `run_md --device gpu`.
## Citation
Ivanova A, Mokshyna O, Polishchuk P.
StreaMD: the toolkit for high-throughput molecular dynamics simulations.
*J. Cheminf.* **2024**, 16 (1), 123.
https://doi.org/10.1186/s13321-024-00918-w