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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.
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...

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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.

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Related Experiment Video

Updated: Jun 3, 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

QSAR modeling of nanomaterials.

Enrico Burello¹, Andrew P Worth

¹Systems Toxicology Unit, Institute for Health and Consumer Protection, Joint Research Centre, European Commission, Ispra, Varese, Italy. Enrico.Burello@ec.europa.eu

Wiley Interdisciplinary Reviews. Nanomedicine and Nanobiotechnology

|March 9, 2011

Summary

Quantitative structure-activity relationship (QSAR) modeling is advancing for nanomaterials. New approaches using nano-QSAR with theoretical and experimental data are crucial for developing safe nanomedicines by understanding nano/bio-interface interactions.

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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

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Last Updated: Jun 3, 2026

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

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Published on: May 9, 2025

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization

Published on: November 12, 2014

Area of Science:

Nanomaterial science
Computational chemistry
Toxicology

Background:

Designing safe and effective nanomedicines requires understanding nanomaterial behavior in biological systems.
Established quantitative structure-activity relationship (QSAR) methods need adaptation for novel nanomaterial properties.
The nano/bio-interface is critical for nanomaterial interactions within biological environments.

Purpose of the Study:

To review recent applications of QSAR modeling for nanomaterials.
To highlight approaches modeling nano/bio-interface interactions.
To discuss the paradigm shift towards nano-QSAR.

Main Methods:

Application of QSAR modeling using both theoretical and experimentally derived descriptors.
Development of nanospecific descriptors to capture unique nanomaterial properties.
Diverse modeling solutions to address the heterogeneity of nanomaterials.

Main Results:

Inspiring recent applications of QSAR for nanomaterials are presented.
Focus on methods describing nano/bio-interface interactions.
Demonstration of diverse modeling strategies reflecting nanomaterial complexity.

Conclusions:

The field is shifting from classic QSAR to nano-QSAR.
Both theoretical descriptor generation and experimental data are vital for nano-QSAR.
Future research should prioritize developing nanospecific descriptors and robust experimental data collection.