Title
SANE: A sequence combined attentive network embedding model for COVID-19 drug repositioning
Abstract
The COVID-19 has now spread all over the world and causes a huge burden for public health and world economy. Drug repositioning has become a promising treatment strategy in COVID-19 crisis because it can shorten drug development process, reduce pharmaceutical costs and reposition approval drugs. Existing computational methods only focus on single information, such as drug and virus similarity or drug-virus network feature, which is not sufficient to predict potential drugs. In this paper, a sequence combined attentive network embedding model SANE is proposed for identifying drugs based on sequence features and network features. On the one hand, drug SMILES and virus sequence features are extracted by encoder-decoder in SANE as node initial embedding in drug-virus network. On the other hand, SANE obtains fields for each node by attention-based Depth-First-Search (DFS) to reduce noises and improve efficiency in representation learning and adopts a bottom-up aggregation strategy to learn node network representation from selected fields. Finally, a forward neural network is used for classifying. Experiment results show that SANE has achieved the performance with 81.98% accuracy and 0.8961 AUC value and outperformed state-of-the-art baselines. Further case study on COVID-19 indicates that SANE has a strong predictive ability since 25 of the top 40 (62.5%) drugs are verified by valuable dataset and literatures. Therefore, SANE is powerful to reposition drugs for COVID-19 and provides a new perspective for drug repositioning. (C) 2021 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.asoc.2021.107831
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Drug-virus interactions, Depth-first-search, Drug repositioning, Attention, Network embedding, COVID-19
Journal
111
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Xiao-Rui Su143.10
Zhuhong You274855.20
Lei Wang300.34
Lun Hu4134.20
Leon Wong503.04
Boya Ji601.69
Bo-Wei Zhao754.12