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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: Jun 12, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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IVGA3D: De novo ligand design using a variable sized tree representation.

Sanghamitra Bandyopadhyay1, Soumi Sengupta

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India. sanghami@isical.ac.in

Protein and Peptide Letters
|June 4, 2010
PubMed
Summary

This study introduces a novel genetic algorithm for de novo ligand design, accelerating drug discovery. The approach efficiently identifies protein active sites and designs effective drug candidates, showing superior performance in experiments.

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Last Updated: Jun 12, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Area of Science:

  • Computational Chemistry
  • Drug Discovery
  • Bioinformatics

Background:

  • Rational drug design is crucial for efficient patient care and accelerates lead molecule discovery.
  • Current methods for de novo ligand design can be time-consuming and costly.

Purpose of the Study:

  • To present a variable string length genetic algorithm with domain-specific operators for de novo ligand design.
  • To evaluate the algorithm's efficiency in mining protein active sites and designing novel ligands.

Main Methods:

  • A genetic algorithm mines protein receptor active sites, guiding ligand construction using 41 molecular fragments.
  • Internal ligand energy and ligand-receptor interaction energy are computed using various physical terms.
  • The algorithm's active site mining is compared against two established detection schemes.

Main Results:

  • The proposed genetic algorithm demonstrates superior performance compared to three other approaches for HIV-1 Protease, HIV-1 Nef, and Thrombin.
  • The designed ligands show effectiveness when compared with known inhibitors for the tested protein targets.

Conclusions:

  • The developed genetic algorithm offers a faster and more cost-effective method for de novo ligand design.
  • This approach holds significant promise for advancing rational drug design and lead molecule discovery.