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Related Concept Videos

Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Introduction to z Scores01:06

Introduction to z Scores

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.
z scores help...
Introduction to z Scores01:05

Introduction to z Scores

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.
z scores help...
z Scores and Unusual Values01:07

z Scores and Unusual Values

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.
 This score indicates how far a value is from the mean in terms of standard deviation. For example, if a data value has a z score of +1, the researcher can infer that the particular data value is one standard deviation above the mean. If another data value...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

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Related Experiment Videos

Unbiasing scoring functions: a new normalization and rescoring strategy.

Giorgio Carta1, Andrew J S Knox, David G Lloyd

  • 1Molecular Design Group, School of Biochemistry and Immunology, Trinity College Dublin, Dublin 2, Ireland.

Journal of Chemical Information and Modeling
|June 8, 2007
PubMed
Summary

Virtual screening often yields false positives due to ligand bias. This study introduces a new method to penalize high molecular weight molecules, improving virtual screening accuracy for targets like estrogen receptor alpha (ERalpha).

Related Experiment Videos

Area of Science:

  • Computational chemistry
  • Drug discovery
  • Molecular modeling

Background:

  • Ligand bias in virtual screening (VS) leads to false positives.
  • Receptor-based docking and scoring functions are crucial for VS.
  • Estrogen receptor alpha (ERalpha) is a key therapeutic target.

Purpose of the Study:

  • To analyze the characteristics of molecules retrieved by various scoring functions.
  • To investigate and mitigate the bias of scoring functions towards complex molecules.
  • To develop improved VS methods for target-specific applications.

Main Methods:

  • Receptor-based docking against ERalpha.
  • Application of multiple scoring functions (ChemGuass, ChemGauss2, ChemScore, ScreenScore, ShapeGauss, PLP) individually and as consensus.
  • Spearman's correlation coefficient to analyze descriptor-hitlist correlations.
  • Introduction of a novel power function to penalize high molecular weight (MW) molecules.
  • Scoring frequency analysis and SIFt fingerprints for VS performance evaluation.

Main Results:

  • Scoring functions show bias towards prioritizing more complex, higher MW molecules.
  • Molecular weight (MW) and other molecular descriptors positively correlate with hitlist rank.
  • The new power function effectively penalizes high MW molecules without affecting lower MW ones.
  • Scoring frequency analysis and SIFt fingerprints provided a more insightful VS performance evaluation than enrichment calculations.

Conclusions:

  • Addressing scoring function bias, particularly concerning molecular weight, is essential for improving VS accuracy.
  • The developed method offers a more meaningful analysis of VS performance.
  • This approach facilitates the development of more effective, target-specific VS strategies.