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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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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 receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Related Experiment Video

Updated: May 29, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Knowledge graph applications and multi-relation learning for drug repurposing: A scoping review.

A Arun Kumar1, Samarth Bhandary1, Swathi Gopal Hegde1

  • 1Department of Biotechnology, PES University, Bangalore 560085, India.

Computational Biology and Chemistry
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

Knowledge graphs offer a unique computational approach to drug repurposing, a cost-effective method for developing new medicines. This nascent field shows recent growth and potential for future drug discovery.

Keywords:
Drug repurposingEmbeddingKnowledge graphMultimodal frameworks

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Area of Science:

  • Computational drug discovery
  • Pharmacology
  • Bioinformatics

Background:

  • Drug development is costly and time-consuming.
  • Drug repurposing offers a cost-effective alternative.
  • Knowledge graphs are emerging as a unique computational tool in this domain.

Purpose of the Study:

  • To review the application of knowledge graphs in medicine.
  • To specifically examine their use in drug repurposing.
  • To understand the trends, applications, and limitations of this approach.

Main Methods:

  • Literature review of scientific papers.
  • Analysis of knowledge graph applications in drug repurposing.
  • Examination of embedding methods, machine learning integration, and knowledge graph completion.

Main Results:

  • 43 papers were analyzed after filtering.
  • Highlighted trends include timeline, geographical distribution, and application areas.
  • Discussed general trends and shortcomings of knowledge graphs in drug repurposing.

Conclusions:

  • Knowledge graph application for drug repurposing is a recent development.
  • The field is currently in its nascent phase.
  • Further research and development are expected.