NeVAE: A Deep Generative Model for Molecular Graphs

TL;DR

NeVAE introduces a novel deep generative model specifically designed for molecular graphs, capable of generating diverse molecules with optimized properties and spatial configurations, outperforming existing methods.

Contribution

The paper presents a specialized variational autoencoder for molecular graphs with a decoder that provides atomic spatial coordinates and an optimization algorithm for property enhancement.

Findings

Discoveries of plausible, diverse, and novel molecules.

Generated molecules with property values 121% higher than state-of-the-art methods.

Effective optimization of molecular spatial configurations for stability.

Abstract

Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with molecular graphs due to their unique characteristics-their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we first propose a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates. Then, we develop a gradient-based algorithm to optimize the decoder of our model so that it learns to generate molecules that maximize the value of certain property of interest and, given a molecule of interest, it is able to optimize the spatial configuration of its atoms for greater stability. Experiments reveal that our variational autoencoder can discover plausible, diverse and novel molecules more effectively than several state of the art models. Moreover, for several properties of interest, our optimized decoder is able to identify molecules with property values 121% higher than those identified by several state of the art methods based on Bayesian optimization and reinforcement learning