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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
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The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
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Related Experiment Video

Updated: Jan 20, 2026

Sign Test for Matched Pairs
01:17

Sign Test for Matched Pairs

385

A Diverse Benchmark Based on 3D Matched Molecular Pairs for Validating Scoring Functions.

Lena Kalinowsky1, Julia Weber1, Shantheya Balasupramaniam2

  • 1Institute of Pharmaceutical Chemistry, Goethe University Frankfurt, Max-von-Laue Str. 9, Frankfurt am Main D-60438, Germany.

ACS Omega
|August 29, 2019
PubMed
Summary

Predicting protein-ligand binding is hard. A new benchmark dataset (3D-MMPs) with 99 molecular pairs helps evaluate docking scoring functions for drug design.

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Related Experiment Videos

Last Updated: Jan 20, 2026

Sign Test for Matched Pairs
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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Predicting protein-ligand interactions and binding free energy is crucial for drug design.
  • Molecular docking and scoring are widely used but scoring accuracy remains a significant challenge.
  • Existing benchmarks may not fully capture the complexity of real-world binding scenarios.

Purpose of the Study:

  • To introduce a diverse benchmark dataset of 99 matched molecular pairs (3D-MMPs) with experimental data.
  • To evaluate the performance of 13 common scoring functions using this new dataset.
  • To establish the 3D-MMP dataset as a valuable resource for benchmarking docking scoring functions.

Main Methods:

  • Compilation of a dataset featuring 99 matched molecular pairs (3D-MMPs).
  • Inclusion of experimentally determined X-ray structures for each pair.
  • Acquisition of corresponding experimental binding affinity data for all pairs.
  • Application of 13 commonly used scoring functions to the 3D-MMP dataset.

Main Results:

  • The study assessed the predictive capabilities of 13 scoring functions on the 3D-MMP dataset.
  • Analysis revealed varying performance levels among the tested scoring functions.
  • The 3D-MMP dataset proved effective in differentiating the performance of scoring functions.

Conclusions:

  • The 3D-MMP dataset is a valuable tool for benchmarking protein-ligand scoring functions.
  • Accurate scoring remains a critical bottleneck in structure-based drug design.
  • This dataset will aid in the development and selection of more reliable scoring methods.