open access

https://hdl.handle.net/2115/76909

Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks

英語

Machine Learning

2019

IEEE (Institute of Electrical and Electronics Engineers)

2019 IEEE International Conference on Big Data (Big Data)

5364-5369

2019 IEEE International Conference will be held 9-12 Dec. 2019 at Los Angeles, CA, USA

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.

会議発表論文

Accepted Manuscript

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