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

Structure-Activity Relationships and Drug Design

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...

You might also read

Related Articles

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

Sort by

Same author

Prediction of activity cliffs on the basis of images using convolutional neural networks.

Journal of computer-aided molecular design·2021

Same author

Evolution of assay interference concepts in drug discovery.

Expert opinion on drug discovery·2021

Same author

Adapting the DeepSARM approach for dual-target ligand design.

Journal of computer-aided molecular design·2021

Same author

Data set of competitive and allosteric protein kinase inhibitors confirmed by X-ray crystallography.

Data in brief·2021

Same author

Evaluation of multi-target deep neural network models for compound potency prediction under increasingly challenging test conditions.

Journal of computer-aided molecular design·2021

Same author

Predicting Isoform-Selective Carbonic Anhydrase Inhibitors via Machine Learning and Rationalizing Structural Features Important for Selectivity.

ACS omega·2021

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

A "Dual-Addition" Calcium Fluorescence Assay for the High-Throughput Screening of Recombinant G Protein-Coupled Receptors

A "Dual-Addition" Calcium Fluorescence Assay for the High-Throughput Screening of Recombinant G Protein-Coupled Receptors

Published on: December 2, 2022

Extraction of structure-activity relationship information from high-throughput screening data.

Mathias Wawer¹, Jürgen Bajorath

¹Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr 2, D-53113 Bonn, Germany.

Current Medicinal Chemistry

|September 17, 2009

Summary

High-throughput screening (HTS) generates vast data, making hit prioritization difficult. This study explores methods to analyze HTS data for structure-activity relationship (SAR) information, aiding hit selection and optimization potential assessment.

More Related Videos

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

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

Published on: May 9, 2025

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

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

Published on: April 3, 2026

Related Experiment Videos

Last Updated: Jun 20, 2026

A "Dual-Addition" Calcium Fluorescence Assay for the High-Throughput Screening of Recombinant G Protein-Coupled Receptors

A "Dual-Addition" Calcium Fluorescence Assay for the High-Throughput Screening of Recombinant G Protein-Coupled Receptors

Published on: December 2, 2022

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

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

Published on: May 9, 2025

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

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

Published on: April 3, 2026

Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

High-throughput screening (HTS) generates large volumes of biological data, presenting challenges in identifying promising drug candidates.
Prioritizing weakly active compounds from HTS campaigns requires effective methods for evaluating preliminary structure-activity relationship (SAR) information.

Purpose of the Study:

To discuss approaches for facilitating HTS data analysis.
To emphasize recent methods for extracting SAR information from screening datasets.
To enable the estimation of chemical optimization potential for selected hits.

Main Methods:

Review of various HTS data analysis techniques.
Focus on methodologies for exploring SAR within screening data.
Discussion of strategies to assess the optimization potential of identified hits.

Main Results:

Identification of challenges in medicinal chemistry due to extensive biological screening data.
Highlighting the importance of SAR information for effective hit selection in HTS.
Presentation of approaches to extract and utilize SAR from screening data.

Conclusions:

Effective analysis of HTS data, particularly SAR exploration, is crucial for efficient drug discovery.
Methods for evaluating chemical optimization potential can significantly support decision-making in hit prioritization.
Advancements in HTS data analysis facilitate better selection of compounds for further development.