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Related Concept Videos

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...
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...
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 30, 2026

Avidity-based Extracellular Interaction Screening (AVEXIS) for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
12:30

Avidity-based Extracellular Interaction Screening (AVEXIS) for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

LigSeeSVM: ligand-based virtual screening using support vector machines and data fusion.

Yen-Fu Chen1, Kai-Cheng Hsu, Po-Tsun Lin

  • 1Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, 30050, Taiwan. yanfu.bi93g@nctu.edu.tw

International Journal of Computational Biology and Drug Design
|July 23, 2011
PubMed
Summary
This summary is machine-generated.

LigSeeSVM is a novel computational tool for drug discovery that improves lead identification using combined molecular descriptors. This ligand-based virtual screening approach demonstrates superior performance compared to existing methods.

Related Experiment Videos

Last Updated: May 30, 2026

Avidity-based Extracellular Interaction Screening (AVEXIS) for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions
12:30

Avidity-based Extracellular Interaction Screening (AVEXIS) for the Scalable Detection of Low-affinity Extracellular Receptor-Ligand Interactions

Published on: March 5, 2012

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Ligand-based virtual screening is crucial for identifying lead compounds, especially when target structures are unavailable.
  • Existing methods can be improved for greater efficiency and accuracy in early-stage drug discovery.

Purpose of the Study:

  • To develop LigSeeSVM, an advanced ligand-based virtual screening tool.
  • To enhance lead discovery by integrating multiple compound descriptor types and machine learning.

Main Methods:

  • Developed LigSeeSVM by combining Support Vector Machines (SVMs) with Atom Pair (AP) and Physicochemical (PC) descriptors.
  • Employed rank-based data fusion to integrate SVM-AP and SVM-PC models.
  • Evaluated LigSeeSVM performance across five diverse datasets.

Main Results:

  • LigSeeSVM significantly outperformed other established ligand-based virtual screening approaches.
  • The integrated model demonstrated enhanced predictive accuracy and effectiveness.

Conclusions:

  • LigSeeSVM offers a powerful and effective computational strategy for lead compound identification.
  • This tool is valuable for accelerating drug discovery pipelines, particularly for structure-lacking targets.