Presentation information

Interactive Session

General Session » Interactive Session

[2Xin5] インタラクティブ1

Wed. Jun 9, 2021 5:20 PM - 7:00 PM Room X (Poster room 1)

[2Xin5-07] Interaction Analysis between Glycoproteins and Drugs by Mutual Attention Neural Networks

〇Eiji Shinkawa1, Koichi Nagatsuka1, Yuki Murata1, Tamiko Ono1, Masae Hosoda1, Kiyoko Flora Kinoshita1, Masayasu Atsumi1 (1.Soka University)

Keywords:Glycan, Glycoprotein, Attention, Neural network

This paper proposes an attention neural network model which aims to predict and analyze interactions between drugs and glycoproteins by focusing on functions of glycans. This model firstly receives drug information, amino acid sequence information, and glycan information as input. Next, it extracts feature vectors via their respective encoders. Then, those features are weighted through mutual attention mechanism, and finally concatenated to detect interaction. The model represents the process by which a glycan mediates the interaction between glycoproteins and drugs through the attention from the glycan to the drug and the attention from the drug to the amino acid sequence. The experimental results show that the proposed mutual attention neural network predicts interactions well and attention analysis suggests candidate interactions between glycans and drugs and between drugs and amino acids.

Authentication for paper PDF access

A password is required to view paper PDFs. If you are a registered participant, please log on the site from Participant Log In.
You could view the PDF with entering the PDF viewing password bellow.