Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Drug Discovery: Overview

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...

Pharmacogenomics: Identification of New Drug Targets

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...

Drug Administration and Therapy Phases: Overview

Drug Administration and Therapy Phases: Overview

Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
The pharmaceutical phase focuses on leveraging the physicochemical properties of the drug to design and manufacture an effective product. Variants include orally administered tablets or capsules, topical creams or ointments, and parenteral-delivery solutions or emulsions.
The pharmacokinetic phase...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A Dataset of Plausible Proton Transfer Steps for Arrow-Pushing Mechanisms.

Scientific data·2026

Same author

A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database.

Journal of cheminformatics·2023

Same author

Fast Substructure Search in Combinatorial Library Spaces.

Journal of chemical information and modeling·2023

Same author

[Hemoptysis under immunosuppression].

Die Anaesthesiologie·2022

Same author

NF-κB drives epithelial-mesenchymal mechanisms of lung fibrosis in a translational lung cell model.

JCI insight·2022

Same author

mGlu3 Metabotropic Glutamate Receptors as a Target for the Treatment of Absence Epilepsy: Preclinical and Human Genetics Data.

Current neuropharmacology·2022

Search research articles

Related Experiment Video

Updated: May 21, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

Integration of distributed computing into the drug discovery process.

Modest von Korff¹, Christian Rufener, Manuel Stritt

¹Actelion Pharmaceuticals Ltd, Gewerbestrasse 16, 4123 Allschwil, Switzerland modest.korff@actelion.com.

Expert Opinion on Drug Discovery

|June 1, 2012

Summary

This summary is machine-generated.

Grid computing provides cost-effective, massive computational power for drug discovery. Seamless integration and user-friendly access to algorithms are key for its successful adoption in research.

More Related Videos

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

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

Published on: December 11, 2016

Related Experiment Videos

Last Updated: May 21, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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

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

Published on: December 11, 2016

Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Drug discovery is a complex, multi-stage process requiring significant computational resources.
Traditional computing infrastructure can be a bottleneck in accelerating drug discovery timelines.
Grid computing offers a scalable and cost-effective solution for high-throughput analysis.

Purpose of the Study:

To introduce grid computing principles and architecture.
To demonstrate the application of grid computing in drug discovery tasks.
To discuss the challenges and future prospects of grid computing in pharmaceutical research.

Main Methods:

Explanation of grid computing principles and architecture.
Exemplification of grid computing use in image processing, molecular docking, and 3D pharmacophore descriptor calculations.
Discussion on company-wide grid installation and integration into research workflows.

Main Results:

Grid computing enables access to massive computing power at reduced costs.
Successful application of grid computing for key drug discovery computational tasks.
Identified challenges in seamless integration and user-friendly access.

Conclusions:

Grid computing holds significant potential to accelerate drug discovery.
Seamless integration into existing research processes is crucial for widespread adoption.
Future efforts should focus on user-friendly access to powerful algorithms without licensing restrictions.