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Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks

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Title: Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks
Authors: Kikuchi, Shoma Browse this author
Takigawa, Ichigaku Browse this author →KAKEN DB
Oyama, Satoshi Browse this author →KAKEN DB
Kurihara, Masahito Browse this author →KAKEN DB
Keywords: Graph Neural Network
Deep Learning
Machine Learning
Issue Date: 2019
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: 2019 IEEE International Conference on Big Data (Big Data)
Start Page: 5364
End Page: 5369
Publisher DOI: 10.1109/BigData47090.2019.9006087
Abstract: Molecular graphs are one of the established representations for small molecules, and even steric or electronic information can be encoded as node and edge features. Naturally, graph neural networks have been intensively investigated to solve various chemical problems at molecular levels. However, it remains unclear how to encode relevant chemical information into graphs. We investigate this problem by proposing three models of graph neural networks with self-attention mechanisms at different levels to adaptively select relevant chemical information for each input. Using neural graph fingerprint (NFP) as a baseline, we introduce three types of attention mechanisms on the top of NFPs. Our experimental evaluations suggest that introducing these self-attention mechanisms contributes to not only improving the prediction accuracy but also providing quantitative interpretation using obtained attention coefficients.
Description: 2019 IEEE International Conference will be held 9-12 Dec. 2019 at Los Angeles, CA, USA
Conference Name: 2019 IEEE International Conference
Conference Place: Los Angeles
Rights: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: proceedings (author version)
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小山 聡

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