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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2

Xiaojuan Shen1, Shijia Yan1, Tao Zeng1

  • 1State Key Laboratory of Anti-Infective Drug Discovery and Development, School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou 510006, China.

Journal of Medicinal Chemistry
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Summary

We developed TarIKGC, a novel tool for drug discovery that uses knowledge graph completion to improve compound-target interaction prediction. TarIKGC successfully identified potential CDK2 inhibitors with antiproliferative activity.

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Area of Science:

  • Biomedical Informatics
  • Computational Drug Discovery
  • Pharmacology

Background:

  • Target identification is crucial in drug discovery, with existing computational methods showing limitations in recommendation performance for compound-target interactions.
  • Improving the prediction of interactions between chemical compounds and biological targets remains a significant challenge in pharmaceutical research.

Purpose of the Study:

  • To introduce TarIKGC, a novel computational tool designed for enhanced target prioritization using semantics-enhanced knowledge graph completion.
  • To improve the recommendation performance of compound-target interactions through advanced knowledge representation learning.

Main Methods:

  • TarIKGC employs knowledge representation learning within a heterogeneous network of compounds, targets, and diseases.
  • It integrates an attention-based aggregation graph neural network with a multimodal feature extractor to learn semantic and topological features.
  • A knowledge graph embedding model is utilized to identify missing relationships between compounds and targets.

Main Results:

  • In silico evaluations demonstrated TarIKGC's superior performance in drug repositioning tasks.
  • The tool successfully identified two novel potential inhibitors of cyclin-dependent kinase 2 (CDK2) via reverse target fishing.
  • These identified compounds exhibited significant antiproliferative activities against CDK2-related therapeutic indications.

Conclusions:

  • TarIKGC offers a powerful approach for target prioritization and drug repositioning by leveraging semantic-enhanced knowledge graph completion.
  • The successful identification and validation of novel CDK2 inhibitors highlight the practical utility of TarIKGC in accelerating drug discovery.