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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.

Huimin Luo1,2, Hui Yang1,2, Ge Zhang1,2

  • 1School of Computer and Information Engineering, Henan University, Kaifeng, China.

Frontiers in Pharmacology
|February 26, 2025
PubMed
Summary

This study introduces KGRDR, a novel deep learning framework for predicting drug-disease interactions. KGRDR enhances drug repositioning by integrating multi-similarity and knowledge graph learning for more accurate therapeutic option discovery.

Keywords:
biomedical knowledge graphdrug repositioningdrug-disease interaction predictionfeature fusionmulti-similarity fusion

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Area of Science:

  • Computational biology
  • Pharmacology
  • Artificial intelligence in medicine

Background:

  • Traditional drug discovery is costly and time-consuming.
  • Drug repositioning offers a faster, cheaper alternative.
  • Predicting novel drug-disease interactions is crucial for optimizing drug development.

Purpose of the Study:

  • To propose a novel deep learning framework, KGRDR, for predicting potential drug-disease interactions.
  • To accelerate the identification of new therapeutic options through computational methods.
  • To reduce the costs and risks associated with drug development.

Main Methods:

  • Developed a deep learning framework (KGRDR) integrating multi-similarity information and knowledge graph learning.
  • Applied a graph regularized approach to fuse drug and disease similarity features.
  • Learned topological features from biomedical knowledge graphs (KGs).
  • Utilized an attention-based feature fusion method.
  • Employed a graph convolutional network for predicting drug-disease associations.

Main Results:

  • KGRDR demonstrated superior performance compared to existing state-of-the-art drug-disease prediction methods.
  • The framework effectively integrated diverse similarity and topological features.
  • Case studies validated KGRDR's capability in identifying novel drug-disease interactions.

Conclusions:

  • KGRDR is an effective deep learning framework for predicting drug-disease interactions.
  • The proposed method enhances drug repositioning efficiency and accuracy.
  • KGRDR shows significant potential for accelerating the discovery of new therapeutic applications.