Optimization of Molecules via Deep Reinforcement Learning

Code

• github.com/google-research/google-research/tree/master/mol_dqn
  Officialtf★ 0
• github.com/danilonumeroso/MEG
  pytorch★ 17
• github.com/junyoung0131/Mol-DQN
  tf★ 0
• github.com/tangxiangru/RL-for-RNA-design
  tf★ 0
• github.com/aksub99/MolDQN-pytorch
  pytorch★ 0
• github.com/caiyingchun/MolDQN
  tf★ 0
• github.com/2023-MindSpore-4/Code12/tree/main/d2l/chapter_11_optimization
  mindspore★ 0

Abstract

We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100\% chemical validity. Further, we operate without pre-training on any dataset to avoid possible bias from the choice of that set. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. We further show the path through chemical space to achieve optimization for a molecule to understand how the model works.

Tasks

Reproductions