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MGATRx: Discovering Drug Repositioning Candidates Using Multi-View Graph Attention.


ABSTRACT: In-silico drug repositioning or predicting new indications for approved or late-stage clinical trial drugs is a resourceful and time-efficient strategy in drug discovery. However, inferring novel candidate drugs for a disease is challenging, given the heterogeneity and sparseness of the underlying biological entities and their relationships (e.g., disease/drug annotations). By integrating drug-centric and disease-centric annotations as multi-views, we propose a multi-view graph attention network for indication discovery (MGATRx). Unlike most current similarity-based methods, we employ graph attention network on the heterogeneous drug and disease data to learn the representation of nodes and identify associations. MGATRx outperformed four other state-of-art methods used for computational drug repositioning. Further, several of our predicted novel indications are either currently investigated or are supported by literature evidence, demonstrating the overall translational utility of MGATRx.

SUBMITTER: Yella JK 

PROVIDER: S-EPMC10038065 | biostudies-literature | 2022 Sep-Oct

REPOSITORIES: biostudies-literature

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MGATRx: Discovering Drug Repositioning Candidates Using Multi-View Graph Attention.

Yella Jaswanth K JK   Jegga Anil G AG  

IEEE/ACM transactions on computational biology and bioinformatics 20220901 5


In-silico drug repositioning or predicting new indications for approved or late-stage clinical trial drugs is a resourceful and time-efficient strategy in drug discovery. However, inferring novel candidate drugs for a disease is challenging, given the heterogeneity and sparseness of the underlying biological entities and their relationships (e.g., disease/drug annotations). By integrating drug-centric and disease-centric annotations as multi-views, we propose a multi-view graph attention network  ...[more]

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