Issue 33, 2020

Machine-guided representation for accurate graph-based molecular machine learning

Abstract

In chemistry-related fields, graph-based machine learning has received significant attention as atoms and their chemical bonds in a molecule can be represented as a mathematical graph. However, many molecular properties are sensitive to changes in the molecular structure. For this reason, molecules have a mixed distribution for their molecular properties in molecular space, and it consequently makes molecular machine learning difficult. However, this problem has not been investigated in either chemistry or computer science. To tackle this problem, we propose a robust and machine-guided molecular representation based on deep metric learning (DML), which automatically generates an optimal representation for a given dataset. To this end, we first adopt DML for molecular machine learning by integrating it with graph neural networks (GNNs) and devising a new objective function for representation learning. In experimental evaluations, machine learning algorithms with the proposed method achieved better prediction accuracy than state-of-the-art GNNs. Furthermore, the proposed method was also effective on extremely small datasets, and this result is impressive because many real world applications suffer from a lack of training data.

Graphical abstract: Machine-guided representation for accurate graph-based molecular machine learning

Supplementary files

Article information

Article type
Paper
Submitted
19 Cax 2020
Accepted
24 Qad 2020
First published
11 Leq 2020

Phys. Chem. Chem. Phys., 2020,22, 18526-18535

Machine-guided representation for accurate graph-based molecular machine learning

G. S. Na, H. Chang and H. W. Kim, Phys. Chem. Chem. Phys., 2020, 22, 18526 DOI: 10.1039/D0CP02709J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements