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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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...
Targets for Drug Action: Overview01:26

Targets for Drug Action: Overview

Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
Receptors are either membrane-spanning or intracellular proteins, which upon binding a ligand, get activated and transmit the signal downstream to elicit a response. Drugs bind receptors, either mimicking the action of endogenous ligands or blocking the receptor activity to bring about a modified response. Nearly 35% of approved drugs target the G...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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 its...
Principles of Drug Action01:24

Principles of Drug Action

Drugs are chemical substances that modify biological responses by interacting with macromolecular targets such as receptors, ion channels, transporters, and enzymes. Pharmacodynamics describes the course of action of drugs leading to the physiological effect at a specific site in the body.
Drugs can be agonists or antagonists. Like the endogenous ligands, agonists always bind and activate the target to produce a cellular response. Agonist binding induces a conformational change which in turn...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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 Kd...

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Related Experiment Video

Updated: May 26, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Visualizing the drug target landscape.

Stephen J Campbell1, Anna Gaulton, Jason Marshall

  • 1Computational Sciences Centre of Emphasis, Pfizer Global Research & Development, Ramsgate Road, Sandwich, Kent CT13 9NJ, UK.

Drug Discovery Today
|December 20, 2011
PubMed
Summary
This summary is machine-generated.

Developing integrated knowledge systems aids drug discovery by visualizing diverse data. This approach helps identify therapeutic opportunities and reduces information overload for researchers.

Related Experiment Videos

Last Updated: May 26, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • Drug discovery requires integrating diverse biological, chemical, and clinical data.
  • Existing tools often focus on single data types, hindering holistic interpretation.
  • A unified system is needed to support decision-making in drug discovery programs.

Purpose of the Study:

  • To describe the development of an integrated knowledge system for drug discovery.
  • To showcase the use of visualization for interpreting complex datasets.
  • To identify pharmaceutical opportunities through data integration.

Main Methods:

  • Development of an organizational knowledge system.
  • Integration of biological, chemical, and clinical data.
  • Application of data visualization techniques.
  • Analysis of disease association, druggability, competitor intelligence, genomics, and text mining data.

Main Results:

  • Creation of distinct 'zones' of pharmaceutical opportunity based on therapeutic precedence.
  • Effective visualization of integrated data, including disease association and druggability.
  • A visual alerting mechanism to filter redundant information and reduce overload.
  • Identification of opportunities in small-molecule repurposing, biotherapeutics, and gene family exploitation.

Conclusions:

  • Integrated knowledge systems are crucial for advancing therapeutic hypothesis generation.
  • Visualization is key to conveying the meaning of complex, integrated datasets.
  • Further development of data standards, technologies, and collaboration is needed.
  • The developed system aids in identifying and evaluating pharmaceutical opportunities more effectively.