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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

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.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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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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Drug-Receptor Bonds01:25

Drug-Receptor Bonds

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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.
In...
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Drug-Receptor Interaction: Agonist01:25

Drug-Receptor Interaction: Agonist

2.5K
Agonists are drugs that interact with specific receptors in the body to produce a biological response. When an agonist binds to a receptor, it activates or enhances the receptor's function, leading to physiological effects. The interaction between agonist drugs and receptors is crucial for their therapeutic action in various medical treatments.
Agonists can bind to receptors in different ways. Some agonists bind directly to the receptor's active site, mimicking the endogenous...
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Drug-Receptor Interaction: Antagonist01:28

Drug-Receptor Interaction: Antagonist

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An antagonist is a drug that binds strongly to a receptor without activating it. An antagonist prevents other molecules, such as neurotransmitters or hormones, from binding to the receptor and triggering a cellular response. Such interaction effectively hinders the normal physiological processes mediated by the receptor, resulting in various pharmacological effects depending on the specific receptor targeted.
Antagonists can be classified as competitive or noncompetitive based on their...
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Related Experiment Video

Updated: Jul 9, 2025

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
05:10

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

Published on: December 11, 2016

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The OREGANO knowledge graph for computational drug repurposing.

Marina Boudin1, Gayo Diallo2, Martin Drancé2

  • 1AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France. marina.boudin@u-bordeaux.fr.

Scientific Data
|December 6, 2023
PubMed
Summary
This summary is machine-generated.

The OREGANO knowledge graph offers a freely available resource for computational drug repositioning. It integrates diverse drug and natural compound data to accelerate drug discovery through link prediction.

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Area of Science:

  • Computational biology
  • Pharmacology
  • Bioinformatics

Background:

  • Drug repositioning accelerates drug discovery compared to traditional methods.
  • Computational approaches using knowledge graphs show promise for generating drug-target hypotheses.
  • A comprehensive, community-accessible knowledge graph integrating broad drug features is lacking.

Purpose of the Study:

  • To introduce the OREGANO knowledge graph, a novel resource for drug repositioning.
  • To incorporate natural compound data into a comprehensive knowledge graph.
  • To provide open access to the knowledge graph and its associated ETL source codes.

Main Methods:

  • Developed a knowledge graph from scratch by integrating data from multiple sources.
  • Designed a graph model and a node merging strategy for data integration.
  • Implemented an Extract, Transform, Load (ETL) process with data cleaning.

Main Results:

  • Successfully constructed the OREGANO knowledge graph, including natural compound data.
  • The knowledge graph and ETL source codes are publicly available on GitHub.
  • The resource facilitates hypothesis generation for drug repositioning via link prediction.

Conclusions:

  • OREGANO addresses the need for a holistic, community-accessible knowledge graph for drug repositioning.
  • The integrated data and open-source nature of OREGANO can advance computational drug discovery.
  • This resource supports faster and more affordable drug development pipelines.