Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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...
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...
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...

You might also read

Related Articles

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

Sort by
Same author

Efficacy, Structure-Activity Relationship, and Mode of Action Studies of a New Generation of Acridine/Acridone-Based Antimalarials.

ACS infectious diseases·2026
Same author

MegaTrans-machine learning models for drug transporters corresponding to the FDA guidance.

Drug metabolism and disposition: the biological fate of chemicals·2026
Same author

Repurposing Clinical Candidates for Nipah and Hendra Viruses.

ACS infectious diseases·2026
Same author

Enhanced Antiviral Activity of Novel Umifenovir Derivatives against SARS-CoV-2: Insights from an International Collaborative Study.

ACS omega·2026
Same author

Novel Small Molecule GLP-1R Agonists Based on 1<i>H</i>-Benzo[<i>d</i>]imidazole-5-Carboxylic Acid Scaffold.

Molecules (Basel, Switzerland)·2026
Same author

Palmitoyl-protein thioesterase-1 in health and disease.

Trends in pharmacological sciences·2026

Related Experiment Video

Updated: Jun 22, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Computational mapping tools for drug discovery.

Yan A Ivanenkov1, Nikolay P Savchuk, Sean Ekins

  • 1ChemDiv Inc., 6605 Nancy Ridge Drive, San Diego, CA 92121, USA.

Drug Discovery Today
|June 13, 2009
PubMed
Summary
This summary is machine-generated.

Computational technologies are revolutionizing drug discovery by handling large, multidimensional datasets. This review explores advanced machine learning mapping techniques for visualizing and analyzing complex chemical and biological data.

Related Experiment Videos

Last Updated: Jun 22, 2026

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Bioinformatics

Background:

  • Computational technologies are integral to modern drug design.
  • Drug discovery now generates vast amounts of multidimensional chemical and biological data.
  • There is a growing need for effective data analysis and representation methods.

Purpose of the Study:

  • To review various dimensionality reduction and data mapping techniques.
  • To highlight methods for analyzing complex, high-dimensional datasets.
  • To present techniques that enable intuitive visual analysis of complex data.

Main Methods:

  • Review of machine learning algorithms.
  • Description of conceptually different mapping techniques.
  • Focus on dimensionality reduction and data representation.

Main Results:

  • Advanced machine learning algorithms have been developed.
  • Various mapping techniques allow for the analysis of complex multidimensional data.
  • Visual representation aids in understanding intricate datasets.

Conclusions:

  • Computational approaches, particularly machine learning, are essential in drug discovery.
  • Mapping techniques offer intuitive ways to analyze and visualize high-dimensional data.
  • These methods facilitate a deeper understanding of complex chemical and biological information.