Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

TL;DR

This paper introduces a symmetry-aware generative neural network for 3D molecular structures that respects rotational invariance, enabling targeted discovery of molecules with desired electronic properties.

Contribution

A novel 3D point set generative model that incorporates symmetry considerations, improving molecular generation and property prediction over existing graph-based approaches.

Findings

Successfully approximates equilibrium molecular structures

Captures complex geometry-property relationships

Biases generation towards molecules with small HOMO-LUMO gaps

Abstract

Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired properties. While graph-based models have previously been proposed, they are restricted by a lack of spatial information such that they are unable to recognize spatial isomerism and non-bonded interactions. Here, we introduce a generative neural network for 3d point sets that respects the rotational invariance of the targeted structures. We apply it to the generation of molecules and demonstrate its ability to approximate the distribution of equilibrium structures using spatial metrics as well as established measures from chemoinformatics. As our model is able to capture the complex relationship between 3d geometry and electronic properties, we bias the distribution of the generator towards molecules with a small HOMO-LUMO gap - an important property for the design of organic solar cells.