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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Structure-Activity Relationships and Drug Design01:28

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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 Biotransformation: Overview01:16

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Pharmaceutical substances known as xenobiotics are predominantly lipophilic and nonionized. This enables them to permeate lipid bilayers, such as cell membranes, and interact with intracellular target receptors. Lipophilic drugs have an advantage in crossing biological barriers and reaching their intended sites of action. However, lipophilic drugs often have a restricted capacity for renal expulsion or elimination from the body. When these drugs enter the kidneys and undergo glomerular...
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Prodrugs01:30

Prodrugs

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Prodrugs are a class of pharmaceutical compounds that undergo a biotransformation process within the body to be converted into a pharmacologically active drug. Prodrugs are designed to improve the therapeutic properties of the parent drug, such as enhancing bioavailability, increasing stability, or reducing toxicity. The concept of prodrugs revolves around modifying the chemical structure of the original drug to make it more effective or convenient for administration.
Prodrugs help overcome...
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Drug Elimination: Non-Renal Routes01:23

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The liver plays a pivotal role in eliminating drugs and their metabolites, primarily through a process known as biliary excretion. This process involves the hepatocytes, the primary cells in the liver that generate bile. A range of transporters actively expels polar drugs or hydrophilic drug metabolites into the bile, which transports the drugs and metabolites into the small intestine. From here, they are eventually expelled from the body through feces. In some instances, the original drug or a...
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Kinetics of Drug Elimination01:17

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Eliminating drugs from the body is a vital process that occurs through excretion or metabolism. Understanding the kinetics of drug elimination is crucial for drug development, dosage determination, and optimizing patient outcomes.
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Explainable drug repurposing via path based knowledge graph completion.

Ana Jiménez1, María José Merino1, Juan Parras2

  • 1Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Avda. Complutense, 30, 28040, Madrid, Spain.

Scientific Reports
|July 18, 2024
PubMed
Summary
This summary is machine-generated.

We developed XG4Repo, an explainable artificial intelligence framework for drug repurposing. It uses biomedical knowledge graphs to identify new uses for existing drugs, providing interpretable paths for validation.

Keywords:
Drug repurposingHeterogeneous knowledge graphsHetionetInterpretabilityKnowledge graph completionRule-based link prediction

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Area of Science:

  • Biomedical Informatics
  • Computational Pharmacology
  • Artificial Intelligence in Drug Discovery

Background:

  • Drug repurposing accelerates the discovery of new therapeutic applications for existing drugs, saving time and cost.
  • Artificial intelligence (AI) and knowledge graphs (KGs) can process vast datasets to identify drug repurposing candidates.
  • Explainability is crucial for validating AI-driven predictions in drug repurposing.

Purpose of the Study:

  • To propose a general architecture for explainable graph completion methods using KGs.
  • To design and present XG4Repo, a novel framework for explainable drug repurposing.
  • To provide interpretable explanations for drug repurposing predictions.

Main Methods:

  • Developed XG4Repo, a framework leveraging biomedical KG connectivity to link compounds with diseases.
  • Implemented automated generation and optimization of multi-type, multi-length metapaths.
  • Focused on generating explanations based on paths connecting compounds to diseases, including intermediate biomedical entities.

Main Results:

  • XG4Repo successfully links compounds to diseases through interpretable paths.
  • The framework utilizes nodes like genes, pathways, side effects, and anatomies for comprehensive explanations.
  • Demonstrated utility through three use cases analyzing Epirubicin, Paclitaxel, and Predinisone repurposing.

Conclusions:

  • XG4Repo enhances the interpretability of AI-driven drug repurposing predictions.
  • The framework facilitates expert validation and further research in drug repurposing.
  • Explainable AI approaches are vital for advancing efficient and reliable drug discovery pipelines.