Metadata-Version: 2.1
Name: conformer-rl
Version: 0.1.0
Summary: Deep Reinforcement Library for Conformer Generation
Home-page: https://github.com/ZimmermanGroup/conformer-rl
Author: Runxuan Jiang
Author-email: runxuanj@umich.edu
License: MIT
Description: # conformer-rl
        An open-source deep reinforcement learning library for conformer generation.
        
        [![Documentation Status](https://readthedocs.org/projects/conformer-rl/badge/?version=latest)](https://conformer-rl.readthedocs.io/en/latest/?badge=latest)
        [![PyPI version](https://badge.fury.io/py/conformer-rl.svg)](https://badge.fury.io/py/conformer-rl)
        
        ## Documentation
        Documentation can be found at https://conformer-rl.readthedocs.io/.
        
        ## Installation
        * We recommend installing in a new conda environment.
          * If you are new to using conda, you can install it [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html) and learn more about environments [here](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)
          * Create and activate a new environment:
            ```
            $ conda create --name conformerrl # create a new environment
            $ conda activate conformerrl # activate the new environment
            ```
        * Install dependencies
          * Install RDKit
        
                $ conda install -c conda-forge rdkit
        
          * We recommend installing the dependencies and versions listed in `requirements.txt`:
            ```
            $ pip install -r requirements.txt
            ```
            The library will most likely still work if you use a different version than what is listed in `requirements.txt`, but most testing was done using these versions.
        
        * Install conformer-rl
        
                $ pip install conformer-rl
        
          * If you did not install dependencies using `requirements.txt`, you will need to manually install Pytorch Geometric [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html).
        
        * Verify Installation: <br />
        As a quick check to verify the installation has succeeded, navigate to the [examples](https://github.com/ZimmermanGroup/conformer-rl/tree/master/examples) directory
        and run `base_example.py`. The script should finish running in a few minutes or less. If no errors ware encountered
        then most likely the installation has succeeded.
        
        ## Features
        
        * Agents - `conformer_rl` contains implementations of agents for several deep reinforcement learning algorithms,
        including recurrent and non-recurrent versions of A2C and PPO. `conformer_rl` also includes a base agent
        interface BaseAgent for constructing new agents.
        
        * Models - Implementations of various graph neural network models are included. Each model is compatible with
        any molecule.
        
        * Environments - Implementations for several pre-built environments that are compatible with any molecule. Environments are built
        on top of the modularized ConformerEnv interface, making it easy to create custom environments
        and max-and-match different environment components.
        
        * Analysis - `conformer_rl` contains a module for visualizing metrics and molecule conformers in Jupyter/IPython notebooks.
        The [example notebook](https://colab.research.google.com/drive/1Y6u4fFM4BkGLtxetZ0QWbR5sZO1U1KPr) in the [examples](https://github.com/ZimmermanGroup/conformer-rl/tree/master/examples) directory shows some examples on how the visualizing tools can be used.
        
        ## Quick Start
        The [examples](https://github.com/ZimmermanGroup/conformer-rl/tree/master/examples) directory contain several scripts for training on pre-built agents and environments.
        Visit [Quick Start](https://conformer-rl.readthedocs.io/en/latest/tutorial/quick_start.html) to get started.
        
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
