robmacc / capstone-molecule-environment

Reinforcement learning environment for inverse drug design.

              _                 _      
  /\/\   ___ | | ___  ___ _   _| | ___ 
 /    \ / _ \| |/ _ \/ __| | | | |/ _ \
/ /\/\ \ (_) | |  __/ (__| |_| | |  __/
\/    \/\___/|_|\___|\___|\__,_|_|\___|
   __           _                                      _   
  /__\ ____   _(_)_ __ ___  _ __  _ __ ___   ___ _ __ | |_ 
 /_\| '_ \ \ / / | '__/ _ \| '_ \| '_ ` _ \ / _ \ '_ \| __|
//__| | | \ V /| | | | (_) | | | | | | | | |  __/ | | | |_ 
\__/|_| |_|\_/ |_|_|  \___/|_| |_|_| |_| |_|\___|_| |_|\__|

Reinforcement learning environment for inverse drug design.

Set-up

Install dependencies:

conda env create -f environment.yml

Activate environment:

conda activate mol-env

Getting started

To get a working gym environment all that's needed is to use the provided repository structure (see here):

• Any dependencies that the environment needs must be defined in setup.py.
• The environment's entry point must be defined in gym_molecule/__init__.py
• The environment needs to be imported into gym_molecule/envs/__init.py__
• With this structure the environment can be installed with pip install -e . from the working directory.
• The environment definition must be written in gym_molecule/envs/molecule_env, and should implement the interface provided by the gym.Env class (see the definition here).
• The essential methods which need definitions are step, reset, render, seed, and close.
• These stubs have been provided in gym_molecule/envs/molecule_env.py.

Testing

Please use pytest to test the environment, an example test file (see tests/environment_test) and a testing workflow script (see .github/workflows/testing) have been provided.