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Related Experiment Video

Updated: Nov 18, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Drug Repurposing for COVID-19 via Knowledge Graph Completion.

Rui Zhang, Dimitar Hristovski, Dalton Schutte

|February 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel literature-based discovery (LBD) method using knowledge graphs to identify potential COVID-19 drug repurposing candidates, uncovering new therapeutic options.

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Last Updated: Nov 18, 2025

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Computational biology
Pharmacology
Artificial intelligence in medicine

Background:

COVID-19 drug discovery requires efficient methods for identifying repurposable agents.
Existing literature-based discovery (LBD) approaches can be enhanced with advanced computational techniques.

Approach:

Developed a neural network-based LBD approach integrating PubMed and COVID-19 literature.
Constructed a knowledge graph from extracted semantic triples using a BERT-based classifier.
Employed five state-of-the-art neural knowledge graph completion models to predict drug candidates.

Key Points:

PubMedBERT-based classifier achieved high accuracy (F1=0.854) in semantic predication.
TransE model demonstrated superior performance in knowledge graph completion.
Identified known and novel drug candidates for COVID-19 repurposing, including paclitaxel and SB 203580.

Conclusions:

The proposed LBD approach is effective for discovering COVID-19 drug candidates and generating mechanistic hypotheses.
This methodology can be generalized for drug discovery in other diseases and clinical questions.