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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...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
Ligand Binding Sites02:40

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence the...

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Related Experiment Video

Updated: May 10, 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

Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches.

Denis Fourches1, Eugene Muratov, Feng Ding

  • 1Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA.

Journal of Chemical Information and Modeling
|July 2, 2013
PubMed
Summary

Ligand-based (2D Quantitative Structure-Activity Relationship) QSAR models and structure-based MedusaDock were evaluated for ranking ligands against protein targets. QSAR models demonstrated competitive accuracy, especially when combined with structure-based methods in consensus approaches.

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Protein Target Prediction and Validation of Small Molecule Compound
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Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

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Last Updated: May 10, 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

Protein Target Prediction and Validation of Small Molecule Compound
10:21

Protein Target Prediction and Validation of Small Molecule Compound

Published on: February 23, 2024

Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Accurate ranking of congeneric ligands is crucial for drug discovery.
  • The CSAR 2011 benchmark provided a dataset to evaluate prediction methods.
  • Both ligand-based and structure-based computational approaches are used for ligand ranking.

Purpose of the Study:

  • To assess the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods.
  • To compare the performance of these methods when used independently and in consensus.
  • To evaluate their effectiveness in ranking ligands against UK, ERK2, and CHK1 protein targets.

Main Methods:

  • Development of an ensemble of 2D QSAR models using the ChEMBL database.
  • Prediction of binding affinity and ranking of CSAR compounds using QSAR models.
  • Application of MedusaDock for predicting docking poses and ranking ligands.
  • Exploration of consensus approaches combining QSAR and MedusaDock predictions.

Main Results:

  • 2D QSAR models achieved high ranking accuracy (Spearman correlation up to 0.78 for UK, 0.60 for ERK2, 0.56 for CHK1), placing predictions in the top 10%.
  • MedusaDock showed lower accuracy (Spearman correlation up to 0.76 for UK, 0.31 for ERK2, 0.26 for CHK1).
  • Consensus approaches improved ranking accuracy, with the best yielding Spearman correlations of 0.82 for UK, 0.50 for ERK2, and 0.45 for CHK1.

Conclusions:

  • Externally validated 2D QSAR models can rank ligands as accurately as computationally intensive structure-based methods.
  • Ligand-based QSAR models effectively complement structure-based approaches, enhancing prediction performance in consensus strategies.
  • These findings support the utility of QSAR in efficient drug discovery pipelines.