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Related Concept Videos

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...
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower Kd...
VSEPR Theory and the Basic Shapes02:52

VSEPR Theory and the Basic Shapes

Overview of VSEPR Theory
2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)01:19

2D NMR: Heteronuclear Single-Quantum Correlation Spectroscopy (HSQC)

Heteronuclear single-quantum correlation spectroscopy (HSQC) is a 2D NMR technique that reveals one-bond correlations between hydrogen and a heteronucleus. The HSQC experiment is similar to the heteronuclear correlation experiment (HETCOR) but is more sensitive. In the HSQC spectrum, the proton chemical shift is plotted on the horizontal F2 axis, while the 13C chemical shift is plotted on the vertical F1 axis. The corresponding proton and 13C spectra are also shown. The HSQC contour plot does...
Reaction Quotient02:35

Reaction Quotient

The status of a reversible reaction is conveniently assessed by evaluating its reaction quotient (Q). For a reversible reaction described by m A + n B ⇌ x C + y D, the reaction quotient is derived directly from the stoichiometry of the balanced equation as
VSEPR Theory02:37

VSEPR Theory

Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...

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Related Experiment Video

Updated: Jun 22, 2026

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

How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR).

J C Dearden1, M T D Cronin, K L E Kaiser

  • 1School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK. j.c.dearden@ljmu.ac.uk

SAR and QSAR in Environmental Research
|June 23, 2009
PubMed
Summary
This summary is machine-generated.

Many quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) analyses contain errors. This study identifies 21 common mistakes and offers solutions to improve QSAR/QSPR research quality.

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Last Updated: Jun 22, 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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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models are crucial tools in drug discovery and chemical research.
  • Despite extensive literature on methodology, numerous published QSAR/QSPR studies contain significant errors.
  • These inaccuracies can lead to unreliable predictions and hinder scientific progress.

Purpose of the Study:

  • To identify and categorize common errors in QSAR/QSPR analyses.
  • To provide a comprehensive discussion of 21 prevalent types of errors found in the QSAR/QSPR literature.
  • To offer practical recommendations for avoiding these errors and enhancing the quality of future QSAR/QSPR studies.

Main Methods:

  • Systematic review and analysis of published QSAR/QSPR literature.
  • Identification and classification of recurring methodological and interpretational errors.
  • Inclusion of illustrative examples, including self-cited instances, to demonstrate error types.

Main Results:

  • Identification of 21 distinct categories of errors frequently observed in QSAR/QSPR studies.
  • Detailed discussion and examples for each identified error type.
  • Highlighting the persistent nature of these errors despite existing guidelines.

Conclusions:

  • A significant number of QSAR/QSPR studies suffer from recurrent errors, compromising their validity.
  • Addressing these 21 identified errors is essential for improving the reliability and reproducibility of QSAR/QSPR models.
  • Implementing the recommended best practices will enhance the scientific rigor and utility of QSAR/QSPR analyses in various fields.