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Related Concept Videos

Applications of Normal Distribution01:22

Applications of Normal Distribution

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The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
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Introduction to z Scores01:06

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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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Introduction to z Scores01:05

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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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Drug Distribution: Plasma Protein Binding01:29

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Drugs predominantly attach to plasma proteins, with only a small percentage remaining unbound. The unbound portion can be calculated as one minus the bound fraction. Acidic drugs form large, inactive complexes by reversibly binding to plasma albumin, which prevents them from diffusing across biological barriers. These drug-protein complexes act as reservoirs for the drugs. As the concentration of unbound drugs decreases, these complexes quickly dissociate to release the free drug, maintaining...
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The z score is one of the three measures of relative standing. It describes the location of a value in a dataset relative to the mean. z scores are obtained after the standardization of the values in a dataset. The z score for the mean is 0.
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A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment.

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    This study introduces a novel scoring-based distributed protein docking application to enhance virtual screening efficiency. The method significantly improves the identification of potential drug leads by prioritizing promising ligands, reducing screening time and cost.

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    Area of Science:

    • Computational chemistry
    • Drug discovery
    • Bioinformatics

    Background:

    • Molecular docking predicts ligand-protein binding modes and energies.
    • Virtual screening uses docking to identify favorable ligands from large chemical libraries.
    • Current methods face challenges in time and cost efficiency.

    Purpose of the Study:

    • To develop a novel scoring-based distributed protein docking application.
    • To improve enrichment of potential drug leads in virtual screening.
    • To address the time and cost limitations of conventional screening methods.

    Main Methods:

    • Developed an automated application for distributed protein docking on a high-performance computing cluster.
    • Implemented a Naïve Bayes scoring function to prioritize ligands based on predicted binding energy.
    • Tested the application on four proteins using a library of 10,573 ligands.

    Main Results:

    • Identified 200 of the top 1,000 binders after docking ~14% of the library.
    • Identified 9-10 best binders after docking ~19% of the library.
    • Observed no significant enrichment after docking ~70% of the library.

    Conclusions:

    • The novel application significantly increases enrichment of potential drug leads in early virtual screening rounds.
    • The scoring-based approach enhances efficiency compared to systematic parallel virtual screening.
    • This method offers a more cost-effective and time-efficient approach to drug discovery.