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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

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...
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Drug Discovery: Overview01:26

Drug Discovery: Overview

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Quantitative Aspects of Drug-Receptor Interaction01:30

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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...
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G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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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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Updated: Oct 19, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery.

Yu-Liang Wang1,2, Jing-Yi Li1,2, Xing-Xing Shi1,2

  • 1Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.

Topics in Current Chemistry (Cham)
|September 23, 2021
PubMed
Summary
This summary is machine-generated.

Quantitative structure-activity relationship (QSAR) web tools accelerate drug discovery by enabling virtual compound evaluation. This survey systematically reviews mainstream QSAR tools to guide researchers in selecting appropriate resources for model development.

Keywords:
DatabaseQSARServerWeb resources

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Area of Science:

  • Computational chemistry and cheminformatics
  • Drug discovery and development
  • Bioinformatics and computational biology

Background:

  • Traditional drug discovery is costly and time-consuming.
  • Quantitative structure-activity relationship (QSAR) methods offer virtual compound evaluation to reduce experimental costs and timelines.
  • Numerous QSAR web tools have emerged over the past two decades, simplifying QSAR application.

Purpose of the Study:

  • To systematically survey and summarize mainstream web tools for Quantitative structure-activity relationship (QSAR) modeling.
  • To provide guidance for researchers in selecting appropriate QSAR tools for model development.
  • To inform the development of future bioinformatics tools leveraging existing QSAR resources.

Main Methods:

  • Systematic survey of available web-based Quantitative structure-activity relationship (QSAR) modeling tools.
  • Evaluation of features and usability of mainstream QSAR web tools.
  • Categorization and comparison of identified QSAR resources.

Main Results:

  • Identification and summary of various Quantitative structure-activity relationship (QSAR) web tools.
  • Analysis of the features and accessibility of these tools.
  • Highlighting the need for a comprehensive overview to aid tool selection.

Conclusions:

  • This systematic review guides researchers in choosing optimal Quantitative structure-activity relationship (QSAR) web tools for their drug discovery projects.
  • The findings can facilitate the development of enhanced bioinformatics tools and promote wider adoption of QSAR methodologies.
  • Increased awareness and accessibility of QSAR tools can accelerate the drug discovery pipeline.