Chemical reaction enhanced graph learning for molecule representation
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
2024-10-01
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
9
Series
Bioinformatics (Oxford, England), Volume 40, issue 10, pp. 1-9
Abstract
MOTIVATION: Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL. RESULTS: In this article, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction. AVAILABILITY AND IMPLEMENTATION: The code is publicly available at https://github.com/coder-ACAC/RLM.Description
Publisher Copyright: © The Author(s) 2024. Published by Oxford University Press.
Keywords
Other note
Citation
Li, A, Casiraghi, E & Rousu, J 2024, ' Chemical reaction enhanced graph learning for molecule representation ', Bioinformatics (Oxford, England), vol. 40, no. 10, btae558, pp. 1-9 . https://doi.org/10.1093/bioinformatics/btae558