Graph Classification of Molecules Using Force Field Atom and Bond Types.
Deep neural network
Force field
Graph kernel
Machine learning
Support vector machine
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
19
11
2018
accepted:
23
09
2019
pubmed:
8
10
2019
medline:
22
12
2020
entrez:
8
10
2019
Statut:
ppublish
Résumé
Classification of the biological activities of chemical substances is important for developing new medicines efficiently. Various machine learning methods are often employed to screen large libraries of compounds and predict the activities of new substances by training the molecular structure-activity relationships. One such method is graph classification, in which a molecular structure can be represented in terms of a labeled graph with nodes that correspond to atoms and edges that correspond to the bonds between these atoms. In a conventional graph definition, atomic symbols and bond orders are employed as node and edge labels, respectively. In this study, we developed new graph definitions using the assignment of atom and bond types in the force fields of molecular dynamics methods as node and edge labels, respectively. We found that these graph definitions improved the accuracies of activity classifications for chemical substances using graph kernels with support vector machines and deep neural networks. The higher accuracies obtained using our proposed definitions can enhance the development of the materials informatics using graph-based machine learning methods.
Identifiants
pubmed: 31589809
doi: 10.1002/minf.201800155
doi:
Substances chimiques
Azepines
0
Benzenesulfonates
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1800155Informations de copyright
© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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