All SMILES Variational Autoencoder

05/30/2019
by   Zaccary Alperstein, et al.
0

Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the computational cost of graph convolutions. To efficiently pass messages along all paths through the molecular graph, we encode multiple SMILES strings of a single molecule using a set of stacked recurrent neural networks, pooling hidden representations of each atom between SMILES representations, and use attentional pooling to build a final fixed-length latent representation. By then decoding to a disjoint set of SMILES strings of the molecule, our All SMILES VAE learns an almost bijective mapping between molecules and latent representations near the high-probability-mass subspace of the prior. Our SMILES-derived but molecule-based latent representations significantly surpass the state-of-the-art in a variety of fully- and semi-supervised property regression and molecular property optimization tasks.

READ FULL TEXT

page 8

page 18

research
05/30/2019

All SMILES VAE

Variational autoencoders (VAEs) defined over SMILES string and graph-bas...
research
02/12/2018

Junction Tree Variational Autoencoder for Molecular Graph Generation

We seek to automate the design of molecules based on specific chemical p...
research
09/08/2018

Molecular Hypergraph Grammar with its Application to Molecular Optimization

This paper is concerned with a molecular optimization framework using va...
research
08/09/2020

Augmenting Molecular Images with Vector Representations as a Featurization Technique for Drug Classification

One of the key steps in building deep learning systems for drug classifi...
research
04/09/2021

A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs

We propose a combination of a variational autoencoder and a transformer ...
research
04/18/2019

Decoding Molecular Graph Embeddings with Reinforcement Learning

We present RL-VAE, a graph-to-graph variational autoencoder that uses re...

Please sign up or login with your details

Forgot password? Click here to reset