ACS Publications. Most Trusted. Most Cited. Most Read
My Activity
Recently Viewed
You have not visited any articles yet, Please visit some articles to see contents here.
CONTENT TYPES

Figure 1Loading Img
RETURN TO ISSUEPREVMachine Learning and...Machine Learning and Deep LearningNEXT

Molecular Property Prediction and Molecular Design Using a Supervised Grammar Variational Autoencoder

Cite this: J. Chem. Inf. Model. 2022, 62, 4, 817–828
Publication Date (Web):February 17, 2022
https://doi.org/10.1021/acs.jcim.1c01573
Copyright © 2022 American Chemical Society
Article Views
777
Altmetric
-
Citations
-
LEARN ABOUT THESE METRICS
Read OnlinePDF (3 MB)
Supporting Info (1)»

Abstract

Abstract Image

Some of the most common applications of machine learning (ML) algorithms dealing with small molecules usually fall within two distinct domains, namely, the prediction of molecular properties and the design of novel molecules with some desirable property. Here we unite these applications under a single molecular representation and ML algorithm by modifying the grammar variational autoencoder (GVAE) model with the incorporation of property information into its training procedure, thus creating a supervised GVAE (SGVAE). Results indicate that the biased latent space generated by this approach can successfully be used to predict the molecular properties of the input molecules, produce novel and unique molecules with some desired property and also estimate the properties of random sampled molecules. We illustrate these possibilities by sampling novel molecules from the latent space with specific values of the lowest unoccupied molecular orbital (LUMO) energy after training the model using the QM9 data set. Furthermore, the trained model is also used to predict the properties of a hold-out set and the resulting mean absolute error (MAE) shows values close to chemical accuracy for the dipole moment and atomization energies, even outperforming ML models designed to exclusive predict molecular properties using the SMILES as molecular representation. Therefore, these results show that the proposed approach is a viable way to provide generative ML models with molecular property information in a way that the generation of novel molecules is likely to achieve better results, with the benefit that these new molecules can also have their molecular properties accurately predicted.

Supporting Information

ARTICLE SECTIONS
Jump To

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jcim.1c01573.

  • Figures of the latent space configuration for all properties, the Pearson correlation coefficient between the true and predicted property values and novel random molecules sampled from the prior, together with their predicted properties for the QM9 data set, the results of the SGVAE using the QM7-X data set, and a briefly discussion of the results of DFT calculations to access the reliability of the model in predicting accurate molecular properties (PDF)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Cited By


This article has not yet been cited by other publications.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

CONTINUE