Symmetry-Aware Actor-Critic for 3D Molecular Design

International Conference on Learning Representations, 2021 Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fund...

Full description

Saved in:
Bibliographic Details
Main Authors Simm, Gregor N. C, Pinsler, Robert, Csányi, Gábor, Hernández-Lobato, José Miguel
Format Journal Article
LanguageEnglish
Published 25.11.2020
Subjects
Computer Science - Learning
Physics - Chemical Physics
Statistics - Machine Learning
Online AccessGet full text

Cover

More Information
Summary:International Conference on Learning Representations, 2021 Automating molecular design using deep reinforcement learning (RL) has the potential to greatly accelerate the search for novel materials. Despite recent progress on leveraging graph representations to design molecules, such methods are fundamentally limited by the lack of three-dimensional (3D) information. In light of this, we propose a novel actor-critic architecture for 3D molecular design that can generate molecular structures unattainable with previous approaches. This is achieved by exploiting the symmetries of the design process through a rotationally covariant state-action representation based on a spherical harmonics series expansion. We demonstrate the benefits of our approach on several 3D molecular design tasks, where we find that building in such symmetries significantly improves generalization and the quality of generated molecules.
DOI:10.48550/arxiv.2011.12747