Metadata-Version: 2.4
Name: CondenSimAdapter
Version: 1.0.4
Summary: An automated workflow for protein condensate simulations, covering the main stages from coarse-grained (CG) to all-atom (AA)
Author-email: Xiaojing Tian <tianxj15@tsinghua.org>
License: GPL-3.0
Project-URL: Homepage, https://github.com/hanlab-computChem/CondenSimAdapter
Project-URL: Documentation, https://github.com/hanlab-computChem/CondenSimAdapter#readme
Project-URL: Repository, https://github.com/hanlab-computChem/CondenSimAdapter
Project-URL: Issues, https://github.com/hanlab-computChem/CondenSimAdapter/issues
Project-URL: Changelog, https://github.com/hanlab-computChem/CondenSimAdapter/blob/main/CHANGELOG.md
Keywords: protein,condensate,simulation,coarse-grained,molecular-dynamics,openmm
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: <3.12,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: click>=8.0
Requires-Dist: click-option-group>=0.5
Requires-Dist: pyyaml>=6.0
Requires-Dist: numpy<2.0,>=1.26
Requires-Dist: tqdm>=4.0
Requires-Dist: pydantic>=2.0
Requires-Dist: mdtraj>=1.10
Requires-Dist: MDAnalysis<3.0,>=2.6
Requires-Dist: biopython>=1.81
Requires-Dist: parmed
Requires-Dist: gromacswrapper
Requires-Dist: scipy>=1.10
Requires-Dist: matplotlib>=3.5
Requires-Dist: networkx>=2.8
Requires-Dist: numba>=0.60
Requires-Dist: jinja2
Requires-Dist: statsmodels
Requires-Dist: PeptideConstructor
Provides-Extra: ml
Requires-Dist: torch<2.5,>=2.4; extra == "ml"
Requires-Dist: dgl==1.1.3; extra == "ml"
Requires-Dist: e3nn>=0.5; extra == "ml"
Requires-Dist: torchdata<0.12,>=0.11; extra == "ml"
Requires-Dist: ml-collections>=0.1; extra == "ml"
Provides-Extra: openmm
Requires-Dist: openmm>=8.2; extra == "openmm"
Provides-Extra: minimal
Provides-Extra: dev
Requires-Dist: pytest>=7; extra == "dev"
Requires-Dist: pytest-cov; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: mypy; extra == "dev"
Dynamic: license-file

# CondenSimAdapter

An automated workflow for protein condensate simulations, covering the main stages from coarse-grained (CG) to all-atom (AA).

## Installation

### Quick Start (Recommended)

**Step 1: Create environment and install heavy dependencies**

```bash
conda create -n condensim python=3.11 -y
conda activate condensim

# PyTorch + DGL (CUDA 12.x)
pip install "torch>=2.4,<2.5" --index-url https://download.pytorch.org/whl/cu124
pip install dgl -f https://data.dgl.ai/wheels/torch-2.4/cu124/repo.html

# OpenMM with CUDA 12
pip install "openmm[cuda12]>=8.2"
```

For other setups:
- **CUDA 13.x**: replace `cu124` with `cu13` and use `openmm[cuda13]`
- **AMD GPU**: `pip install "openmm[hip6]>=8.2"` or `openmm[hip7]`
- **CPU only**: use `--index-url https://download.pytorch.org/whl/cpu` and plain `openmm>=8.2`

**Step 2: Install CondenSimAdapter**

```bash
pip install CondenSimAdapter
```

**Step 3: Download neural network models (~180 MB)**

```bash
adapter models download
```

Models are hosted on GitHub Releases and cached locally.

**Step 4: Verify**

```bash
adapter --help
adapter info
adapter models status
```

### Development Installation

```bash
git clone https://github.com/hanlab-computChem/CondenSimAdapter.git
cd CondenSimAdapter

# Install heavy deps (Step 1 above), then:
pip install -e ".[ml,openmm,dev]"

# Download models
adapter models download

# Run tests
pytest tests/unit -v
```

## Usage

### Typical Workflow

```bash
# 1. Create project configuration
adapter init my_project --topol cubic -c FUS:10

# 2. Run CG simulation
adapter cg -f config.yaml

# 3. Backmap to all-atom
adapter backmap -i output_CG -f config.yaml

# 4. Energy minimization
adapter minimize -i output_backmap -f config.yaml

# 5. Generate production run scripts
adapter to_run -f config.yaml
```

### CLI Commands

```
CORE COMMANDS:
    cg               Run coarse-grained simulation
    backmap          Backmap CG structure to all-atom representation
    minimize         Energy minimization with AMBER/CHARMM force fields
    to_run           Generate production run scripts for minimize output

UTILITY COMMANDS:
    init             Initialize a new configuration template
    droplet-density  Estimate protein density in droplet geometry
    info             Display system and environment information
    forcefield       Manage custom all-atom force fields
    models           Manage neural network models for backmapping
```

## Requirements

- Python >= 3.10, < 3.12 (3.11 recommended)
- Linux x86_64
- CUDA >= 12.x capable GPU (optional, CPU mode works too)
- GROMACS >= 2023 (for topology preparation)

## Package Structure

| Component | Size | Notes |
|-----------|------|-------|
| Source code + data | ~3 MB | Installed via pip |
| Neural network models | ~235 MB | Downloaded via `adapter models download` |
| PyTorch + DGL + OpenMM | ~2 GB | Installed via pip (Step 1) |

## Links

- PyPI: https://pypi.org/project/CondenSimAdapter/
- GitHub: https://github.com/hanlab-computChem/CondenSimAdapter
- Issues: https://github.com/hanlab-computChem/CondenSimAdapter/issues

## License

GPL-3.0
