Searching for High-Value Molecules Using Reinforcement Learning and
  Transformers
Raj Ghugare Santiago Miret Adriana Hugessen Mariano Phielipp Glen Berseth
Reinforcement learning (RL) over text representations can be effective for
finding high-value policies that can search over graphs. However, RL requires
careful structuring of the search space and algorithm design to be effective in
this challenge. Through extensive experiments, we explore how different design
choices for text grammar and algorithmic choices for training can affect an RL
policy's ability to generate molecules with desired properties. We arrive at a
new RL-based molecular design algorithm (ChemRLformer) and perform a thorough
analysis using 25 molecule design tasks, including computationally complex
protein docking simulations. From this analysis, we discover unique insights in
this problem space and show that ChemRLformer achieves state-of-the-art
performance while being more straightforward than prior work by demystifying
which design choices are actually helpful for text-based molecule design.