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Structure-Activity Relationships and Drug Design

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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.
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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

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Related Experiment Video

Updated: Jul 4, 2026

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

Distribution of randomly generated activity class characteristic substructures in diverse active and database

José Batista¹, Jürgen Bajorath

¹Department of Life Science Informatics, B-IT, LIMES Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstr. 2, 53113, Bonn, Germany.

Molecular Diversity

|May 29, 2008

Summary

Activity Class Characteristic Substructures (ACCS) offer potent molecular descriptors for drug discovery. Their effectiveness stems from rare combinations within screening databases, outperforming complex fingerprints in similarity searches.

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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

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Last Updated: Jul 4, 2026

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

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Area of Science:

Chemoinformatics
Medicinal Chemistry
Computational Drug Discovery

Background:

Substructure descriptors are vital in chemoinformatics and medicinal chemistry.
Conventional descriptors often rely on predefined design strategies.
Activity Class Characteristic Substructures (ACCS) were previously introduced, derived from random fragment populations.

Purpose of the Study:

To analyze the distribution of ACCS across diverse chemical spaces.
To understand the reasons behind the high predictive utility of ACCS.
To compare ACCS performance against established 2D fingerprints.

Main Methods:

Analysis of ACCS distribution in nine activity classes.
Examination of nearly four million chemical database compounds.
Evaluation of ACCS fingerprints in similarity searching.

Main Results:

Short ACCS fingerprints demonstrate high performance across various compound classes.
ACCS exhibit surprisingly strong results compared to advanced 2D fingerprints.
The discriminatory power of ACCS is linked to their infrequent co-occurrence in databases.

Conclusions:

ACCS are effective molecular descriptors due to their unique distribution patterns.
The simplicity and rare occurrence of ACCS contribute to their predictive power.
ACCS represent a valuable tool for similarity searching in drug discovery.