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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...

Quantitative Aspects of Drug-Receptor Interaction

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...

The Equilibrium Binding Constant and Binding Strength

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:

Protein-Drug Binding: Determination Methods

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of the aromatic...

Drug-Receptor Interactions

Drug-Receptor Interactions

Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue.

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Related Experiment Video

Updated: Jun 1, 2026

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

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

Published on: August 28, 2019

Androgen receptor binding affinity: a QSAR evaluation.

M Todorov¹, E Mombelli, S Ait-Aissa

¹Laboratory of Mathematical Chemistry, Bourgas As. Zlatarov University, Bourgas, Bulgaria.

SAR and QSAR in Environmental Research

|May 21, 2011

Summary

This study developed a computational model to predict androgen receptor (AR) binding affinity based on molecular structure. The model accurately identifies chemicals with potential AR activity, aiding in safety assessments.

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

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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

Area of Science:

Computational Chemistry
Molecular Modeling
Pharmacology

Background:

Understanding the structural basis of ligand-receptor interactions is crucial for drug discovery and safety assessment.
The androgen receptor (AR) plays a vital role in various physiological processes, and its modulation by chemicals has significant implications.

Purpose of the Study:

To develop and validate a multiparameter computational model (COREPA) for predicting rat androgen receptor (AR) binding affinity.
To establish a screening tool for identifying chemicals with potential AR activity based on structural and electronic properties.

Main Methods:

Utilized the COmmon REactivity PAttern (COREPA) approach, incorporating molecular flexibility, nucleophilic sites, electronic, and hydrophobic interactions.
Developed categorical models correlating binding affinity with specific molecular descriptors (distances, delocalizability, partial positive surface areas, HOMO energy, log Kow).
Validated the model using external datasets and a novel chemical selection technique, followed by experimental testing of selected compounds for AR transcriptional activation.

Main Results:

A robust screening battery of models was constructed, achieving a high Pearson contingency coefficient of 0.9.
The model demonstrated strong predictability on external validation sets and within its applicability domain.
Experimental validation confirmed the theoretical predictions, showing that the model can accurately identify potential AR modulators.

Conclusions:

The COREPA approach provides a reliable method for predicting AR binding affinity and identifying potential androgenic chemicals.
This computational tool can significantly aid in the early screening of chemicals for potential endocrine-disrupting activity.