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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...

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Updated: May 26, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Virtual screening data fusion using both structure- and ligand-based methods.

Fredrik Svensson1, Anders Karlén, Christian Sköld

  • 1Organic Pharmaceutical Chemistry, Department of Medicinal Chemistry, BMC, Uppsala University, P.O. Box 574, SE-751 23 Uppsala, Sweden.

Journal of Chemical Information and Modeling
|December 14, 2011
PubMed
Summary
This summary is machine-generated.

Data fusion significantly enhances virtual screening for drug discovery by improving compound ranking. Parallel selection, rank voting, and Pareto ranking algorithms show promising results over single methods.

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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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Last Updated: May 26, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
06:26

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
08:35

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source

Published on: May 29, 2021

Area of Science:

  • Computational chemistry
  • Medicinal chemistry
  • Bioinformatics

Background:

  • Virtual screening is a crucial technique in drug discovery for identifying potential drug candidates.
  • Current virtual screening methods require continuous improvement for better performance and consistency.

Purpose of the Study:

  • To evaluate the effectiveness of five data fusion algorithms in improving compound ranking for virtual screening.
  • To compare the performance of data fusion methods against individual virtual screening techniques.

Main Methods:

  • Evaluated five data fusion algorithms: sum rank, rank vote, sum score, Pareto ranking, and parallel selection.
  • Utilized 16 datasets generated from docking, pharmacophore search, shape similarity, and electrostatic similarity methods.
  • Included both structure-based and ligand-based virtual screening approaches.

Main Results:

  • Data fusion demonstrably improved the performance and consistency of compound ranking in virtual screening.
  • Parallel selection emerged as the top-performing data fusion algorithm.
  • Rank voting and Pareto ranking also exhibited strong performance in compound prioritization.

Conclusions:

  • Data fusion is a valuable strategy for enhancing virtual screening efficiency and reliability in drug discovery.
  • The parallel selection algorithm offers a superior approach for data fusion in virtual screening.
  • Rank voting and Pareto ranking provide viable alternatives for improving virtual screening outcomes.