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

13.6K
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...
13.6K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

2.2K
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...
2.2K

You might also read

Related Articles

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

Sort by
Same author

Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.

ACS pharmacology & translational science·2026
Same author

The Use of Deep Learning in RNA Therapeutic Development.

ACS nano·2026
Same author

Plug-and-play assembly of biodegradable ionizable lipids for potent mRNA delivery and gene editing in vivo.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Profiling biological effects of microbiome metabolites via machine learning.

iScience·2026
Same author

Identification of 4,5,6,7-Tetrabromo-1<i>H</i>-benzotriazole (TBB) as a Small Molecule MESH1 Inhibitor that Suppresses Ferroptosis.

bioRxiv : the preprint server for biology·2026
Same author

Genetically Encoded Sterol-Modification of a Synthetic Intrinsically Disordered Protein Drives Its Self-Assembly Into Diverse Morphologies.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: Apr 19, 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

236

Active-learning strategies in computer-assisted drug discovery.

Daniel Reker1, Gisbert Schneider1

  • 1Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.

Drug Discovery Today
|December 16, 2014
PubMed
Summary

Active-learning methods optimize drug discovery by intelligently selecting compounds for testing, reducing costs and saving resources. These computational approaches adaptively refine molecule selection for more efficient screening cycles.

More Related Videos

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.7K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

2.7K

Related Experiment Videos

Last Updated: Apr 19, 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

236
Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

5.7K
Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

2.7K

Area of Science:

  • Computational chemistry
  • Drug discovery informatics

Background:

  • High-throughput screening (HTS) is resource-intensive.
  • Selecting promising drug candidates requires significant effort and time.
  • Existing methods often involve screening large compound libraries.

Purpose of the Study:

  • To provide a comprehensive overview of computational active-learning (AL) approaches.
  • To outline the potential of AL methods in drug discovery.
  • To highlight how AL can improve compound selection efficiency.

Main Methods:

  • Active-learning algorithms adaptively guide the screening process.
  • Focused subsets of compounds are tested based on feedback.
  • Structure-activity landscapes are refined through iterative cycles.

Main Results:

  • Active learning prioritizes areas of chemical space with high success probability.
  • These methods consider structural novelty in compound selection.
  • Potential to significantly reduce screening costs and material usage.

Conclusions:

  • Computational active-learning offers a powerful strategy for efficient drug discovery.
  • AL methods can accelerate the identification of promising drug candidates.
  • The adaptive nature of AL enhances the optimization of screening campaigns.