Graph Classification of Molecules Using Force Field Atom and Bond Types. Issue 1 (7th October 2019)

• Record Type: Journal Article
• Authors: Jippo, Hideyuki; Matsuo, Tatsuru; Kikuchi, Ryota; Fukuda, Daisuke; Matsuura, Azuma; Ohfuchi, Mari
• Is Part Of: Molecular informatics. Volume 39:Issue 1/2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-10-07
• Subjects: Machine learning -- Force field -- Graph kernel -- Support vector machine -- Deep neural network; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201800155
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: 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. Abstract :