conformer-rl 0.0.1

Deep Reinforcement Library for Conformer Generation

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 to get started.