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Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction

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journal contribution
posted on 2024-05-29, 01:24 authored by Honglei Bai, Siyuan Lu, Tiangang Zhang, Hui CuiHui Cui, Toshiya Nakaguchi, Ping Xuan

Summary: Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches. 

Funding

This work is supported by the Natural Science Foundation of China (62172143, 62372282), STU Scientific Research Initiation grant (NTF22032), and the Natural Science Foundation of Heilongjiang Province (LH2023F044).

History

Publication Date

2024-06-21

Journal

iScience

Volume

27

Issue

6

Article Number

109571

Pagination

15p.

Publisher

Elsevier

ISSN

2589-0042

Rights Statement

© 2024 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).