NeVAE: A Deep Generative Model for Molecular Graphs

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. *

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

“…The literature concerning generative models of molecules has exploded since the first work on the topic Gómez-Bombarelli et al [2018]. Current methods feature molecular representations such as SMILES [Janz et al, 2018, Segler et al, 2017, Skalic et al, 2019, Ertl et al, 2017, Lim et al, 2018, Kang and Cho, 2018, Sattarov et al, 2019, Gupta et al, 2018, Harel and Radinsky, 2018, Yoshikawa et al, 2018, Bjerrum and Sattarov, 2018, Mohammadi et al, 2019 and graphs [Simonovsky and Komodakis, 2018, Li et al, 2018a, De Cao and Kipf, 2018, Kusner et al, 2017, Dai et al, 2018, Samanta et al, 2019, Li et al, 2018b, Kajino, 2019, Jin et al, 2019, Bresson and Laurent, 2019, Lim et al, 2019, Pölsterl and Wachinger, 2019, Krenn et al, 2019, Maziarka et al, 2019, Madhawa et al, 2019, Shen, 2018, Korovina et al, 2019 In this section we conduct an empirical test of the hypothesis from [Gómez-Bombarelli et al, 2018] that the decoder's lack of efficiency is due to data point collection in "dead regions" of the latent space far from the data on which the VAE was trained. We use this information to construct a binary classification Bayesian Neural Network (BNN) to serve as a constraint function that outputs the probability of a latent point being valid, the details of which will be discussed in the section on labelling criteria.…”

EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation

“…The other common representation is the molecular graph. It can be extracted from the latent space of deep learning methods [ 12 , 13 , 22 ]. It can also be built sequentially, using reinforcement learning [ 15 , 23 – 25 ] or using evolutionary algorithms [ 27 ].…”

“…In fact, several deep learning architectures were then published for de novo molecular generation. Among them, autoencoders are trained to convert points of their latent space to SMILES [ 7 , 9 – 11 ] or molecular graphs [ 12 , 13 ]. Their latent space can then be explored to provide new solutions.…”

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

“…In reality, molecules that have similar structural-activity properties can have a different number of atoms and bonds. Samanta et al [201] developed NeVAE, another deep generative model for molecular graphs, and this model allowed for graphs with different numbers of nodes and edges.…”