Metadata-Version: 2.1
Name: deepdrivemd
Version: 0.0.1
Summary: Deep-Learning Driven Adaptive Molecular Simulations
Home-page: https://deepdrivemd.github.io/
Author: Alexander Brace, Hyungro Lee, Heng Ma, Anda Trifan, Matteo Turilli, Igor Yakushin, Li Tan, Andre Merzky, Tod Munson, Ian Foster, Shantenu Jha, Arvind Ramanathan
Author-email: ramanathana@anl.gov, shantenu.jha@rutgers.edu
License: MIT License
Keywords: workflow,machine-learning,complex-biological-systems,multi-scale-molecular-simulations
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: pyyaml
Requires-Dist: pydantic
Requires-Dist: radical.entk
Requires-Dist: MDAnalysis
Requires-Dist: numpy
Requires-Dist: h5py (==2.10.0)

# DeepDriveMD-F (DeepDriveMD-pipeline)

DeepDriveMD-F: Deep-Learning Driven Adaptive Molecular Simulations (file-based continual learning loop)

[![Documentation Status](https://readthedocs.org/projects/deepdrivemd-pipeline/badge/?version=latest)](https://deepdrivemd-pipeline.readthedocs.io/en/latest/?badge=latest)

Details can be found in the [ducumentation](https://deepdrivemd-pipeline.readthedocs.io/en/latest/). For more information, please see our [website](https://deepdrivemd.github.io/).

## How to run

### Setup

Install `deepdrivemd` into a virtualenv with:

```
python3 -m venv env
source env/bin/activate
pip install --upgrade pip setuptools wheel
pip install -e .
```

Then, install pre-commit hooks: this will auto-format and auto-lint _on commit_ to enforce consistent code style:

```
pre-commit install
pre-commit autoupdate
```

In some places, DeepDriveMD relies on external libraries to configure MD simulations and import specific ML models.

For MD, install the `mdtools` package found here: https://github.com/braceal/MD-tools

For ML (specifically the AAE model), install the `molecules` package found here: https://github.com/braceal/molecules/tree/main

### Generating a YAML input spec:

First, run this command to get a _sample_ YAML config file:

```
python -m deepdrivemd.config
```

This will write a file named `deepdrivemd_template.yaml` which should be adapted for the experiment at hand. You should configure the `molecular_dynamics_stage`, `aggregation_stage`, `machine_learning_stage`, `model_selection_stage` and `agent_stage` sections to use the appropriate run commands and environment setups.

### Running an experiment

Then, launch an experiment with:

```
python -m deepdrivemd.deepdrivemd -c <experiment_config.yaml>
```

This experiment should be launched

### Note on input data

The input PDB and topology files should have the following structure:

```
ls data/sys*

data/sys1:
comp.pdb comp.top

data/sys2:
comp.pdb comp.top
```
Where the topology files are optional and only used when `molecular_dynamics_stage.task_config.solvent_type` is "explicit". Only one system directory is needed but an arbitrary number are supported. Also note that the system directory names are arbitrary. The path to the `data` directory should be passed into the config via `molecular_dynamics_stage.initial_pdb_dir`.

MIT License

Copyright (c) 2021 DeepDriveMD-F

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


