Metadata-Version: 2.1
Name: conformer-rl
Version: 0.0.1
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
        Documentation can be found at https://conformer-rl.readthedocs.io/.
        
        ## Installation
        
        * Prerequisites
          * Install RDKit
        
                $ conda install -c conda-forge rdkit
        
          * Install PyTorch Geometric. Since the installation is heavily dependent on the PyTorch, OS and CUDA versionsof the system, detailed instructions for installing PyTorch Geometric can be found at https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html.
        
        * Install conformer-rl
        
                $ pip install conformer-rl
        
        * Verify Installation
        As a quick check to verify the installation has succeeded, navigate to the `examples` directory
        and run `test_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.
        `examples/example_analysis.ipynb` shows some examples on how the visualizing tools can be used.
        
        ## Quick Start
        The `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
