Deep learning of multimodal networks with topological regularization for drug repositioning

STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git .

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Publication Details

Journal
Journal of Cheminformatics
Published
2024-08-23
DOI
https://doi.org/10.1186/s13321-024-00897-y
Citations
11
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.98

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article

Deep learning of multimodal networks with topological regularization for drug repositioning

Manato Akiyama, Yuto Ohnuki, Yasubumi Sakakibara
11 citations
Journal of Cheminformatics
Computational Drug Discovery Methods
3.98
article

Deep learning of multimodal networks with topological regularization for drug repositioning

Manato Akiyama, Yuto Ohnuki, Yasubumi Sakakibara
article en
11 citations

Abstract

STRGNN demonstrates superior accuracy compared to existing methods and has identified several novel drug effects, corroborating existing literature. STRGNN emerges as a powerful tool for drug prediction and discovery. The source code for STRGNN, along with the dataset for performance evaluation, is available at https://github.com/yuto-ohnuki/STRGNN.git .

Journal of CheminformaticsVol. 16(1)
Keio University (JP)
Ministry of Education, Culture, Sports, Science and Technology
Openalex Percentile: Top 6%
Computational Drug Discovery Methods
3.98
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