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Applying graph neural network models to molecular property prediction using high-quality experimental data

Published

Author(s)

Barry I. Schneider, Anthony J. Kearsley, Walid Keyrouz, Thomas C. Allison, chen qu

Abstract

Graph neural networks have been successfully applied to machine learning models related to molecules and crystals, due to the similarity between a molecule/crystal and a graph. In this paper, we present three models that are trained with high-quality experimental data to predict three molecular properties (Kováts retention index, normal boiling point, and mass spectrum), using the same GNN architecture. We show that graph representations of molecules, combined with deep learning methodologies and high-quality data sets, lead to accurate machine learning models to predict molecular properties.
Citation
Artificial Intelligence Chemistry

Keywords

Kov´ats retention index, boiling point, mass spectrum, graph neural network, deep learning

Citation

Schneider, B. , Kearsley, A. , Keyrouz, W. , Allison, T. and qu, C. (2024), Applying graph neural network models to molecular property prediction using high-quality experimental data, Artificial Intelligence Chemistry, [online], https://doi.org/10.1016/j.aichem.2024.100050., https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956419 (Accessed April 14, 2024)
Created June 1, 2024, Updated March 28, 2024