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A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

ABSTRACT: The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Networks to construct the micro-to-macro drug centric heterogeneous network (DSMN), and develop a multi-branches HyperGraph learning model, called HGDrug, for Drug multi-task predictions. HGDrug achieves highly accurate and robust predictions on 4 benchmark tasks (drug-drug, drug-target, drug-disease, and drug-side-effect interactions), outperforming 8 state-of-the-art task specific models and 6 general-purpose conventional models. Experiments analysis verifies the effectiveness and rationality of the HGDrug model architecture as well as the multi-branches setup, and demonstrates that HGDrug is able to capture the relations between drugs associated with the same functional groups. In addition, our proposed drug-substructure interaction networks can help improve the performance of existing network models for drug-related prediction tasks.

SUBMITTER: Jin S

PROVIDER: S-EPMC10681315 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

Jin Shuting S Hong Yue Y Zeng Li L Jiang Yinghui Y Lin Yuan Y Wei Leyi L Yu Zhuohang Z Zeng Xiangxiang X Liu Xiangrong X

PLoS computational biology 20231113 11

The powerful combination of large-scale drug-related interaction networks and deep learning provides new opportunities for accelerating the process of drug discovery. However, chemical structures that play an important role in drug properties and high-order relations that involve a greater number of nodes are not tackled in current biomedical networks. In this study, we present a general hypergraph learning framework, which introduces Drug-Substructures relationship into Molecular interaction Ne ...[more]

PMID: 37956212

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Dataset Information

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

Publications

A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks.

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