Generating stable molecules using imitation and reinforcement learning

Søren Ager Meldgaard

doi:10.1088/2632-2153/ac3eb4

Generating stable molecules using imitation and reinforcement learning

2021-12-10

9

OA

AI

Henrik Lund Mortensen

M

Mads-Peter Verner Christiansen

DOI：10.1088/2632-2153/ac3eb4

Original

Original request for help

Save

Abstract

En 中文

Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these methods often rely on a graph representation, ignoring 3D information necessary for determining the stability of the molecules. We propose a reinforcement learning (RL) approach for generating molecules in Cartesian coordinates allowing for quantum chemical prediction of the stability. To improve sample-efficiency we learn basic chemical rules from imitation learning (IL) on the GDB-11 database to create an initial model applicable for all stoichiometries. We then deploy multiple copies of the model conditioned on a specific stoichiometry in a RL setting. The models correctly identify low energy molecules in the database and produce novel isomers not found in the training set. Finally, we apply the model to larger molecules to show how RL further refines the IL model in domains far from the training data.

Keywords:

reinforcement learning

chemical physics

chemical space

global optimization

imitation learning

Generating stable molecules using imitation and reinforcement learning

Abstract

Journal

Organization