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A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
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z Scores and Area Under the Curve01:17

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z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
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Inverse trigonometric functions are fundamental mathematical tools that reverse the actions of standard trigonometric functions. While trigonometric functions map angles to ratios, inverse trigonometric functions perform the opposite operation by mapping a ratio back to its corresponding angle. These functions are essential in various applications, particularly in determining angles when given specific distances, such as calculating elevation angles in navigation and engineering.For a function...
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The shape of a suspension bridge cable hanging under its own weight is described by a catenary curve, which is modeled using the hyperbolic cosine function. This mathematical model accurately captures the balance between gravity and tension acting along the cable. When a particular vertical position on the cable is known, the corresponding horizontal position can be determined using the inverse hyperbolic cosine function, allowing for a detailed analysis of the cable's geometry.Inverse...
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A ship tracking an approaching aircraft relies on geometric measurements to find out the aircraft’s position relative to the observer. By measuring the slant distance to the aircraft and the angle of elevation, the horizontal and vertical components of the distance can be obtained using trigonometric relationships. This geometric approach provides a basis for analyzing how the observed angle changes as the aircraft moves closer to the ship.To examine the mathematical behavior of the angle...
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Improving inverse docking target identification with Z-score selection.

Stephanie S Kim1, Melanie L Aprahamian1, Steffen Lindert1

  • 1Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio.

Chemical Biology & Drug Design
|January 4, 2019
PubMed
Summary
This summary is machine-generated.

A new combined Z-score method improves target identification accuracy in drug discovery by integrating ligand and receptor data. This approach enhances prediction success rates and offers a user-friendly Python script for broader accessibility.

Keywords:
Z-scoreinverse dockingmolecular dockingvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Inverse docking is crucial for identifying drug targets and predicting side effects.
  • Current inverse docking methods often suffer from low accuracy and limited usability.
  • Distinguishing true targets from potential candidates remains a significant challenge.

Purpose of the Study:

  • To develop a more accurate and user-friendly method for ligand-protein target identification.
  • To overcome the limitations of existing inverse docking techniques, particularly the poor indication of absolute docking scores.

Main Methods:

  • Developed a novel "combined Z-score" method, weighting ligand and receptor-based Z-scores.
  • Validated the method on Astex, DUD, and DUD-E benchmark datasets.
  • Created a user-friendly Python script for implementing the combined Z-score analysis.

Main Results:

  • The combined Z-score method significantly improved prediction accuracy across all tested datasets (14%, 3.6%, and 6.3% increase).
  • Achieved the highest Area Under the Curve (AUC) in ROC analysis compared to docking score-based selection.
  • Demonstrated superior enrichment factors for top predictions on Astex and DUD-E datasets.

Conclusions:

  • The combined Z-score method offers a more reliable approach to target identification in drug discovery.
  • The developed Python script enhances accessibility for researchers, facilitating wider adoption.
  • This advancement aids in more efficient and accurate detection of potential drug targets and side effects.