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,

SUBMITTER: Jin S 

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

REPOSITORIES: biostudies-literature