Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to z Scores01:06

Introduction to z Scores

10.9K
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...
10.9K
Introduction to z Scores01:05

Introduction to z Scores

1.2K
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...
1.2K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

18.4K
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...
18.4K
z Scores and Unusual Values01:07

z Scores and Unusual Values

10.9K
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...
10.9K
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

405
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
405
Structural Protein Function01:56

Structural Protein Function

29.8K
Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
29.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Viral Sentry AI-Automated zoonotic surveillance and drug repurposing agent.

Biology methods & protocols·2026
Same author

Rambutan (<i>Nephelium lappaceum</i> L.) Shell as a Source of Polyphenols: Chemical Characterization and Biological Activities.

Molecules (Basel, Switzerland)·2026
Same author

Global integration, local constraints: how internationalized scientific development shapes early-career research in Latin America.

Frontiers in sociology·2026
Same author

Phylogenomic discordances reveal conflicting hybridization episodes and widespread lineage-sorting events in temperate Loliinae grasses.

Systematic biology·2026
Same author

Interkingdom horizontal gene transfer in plants: a perspective on methodological limitations and evolutionary alternatives.

Frontiers in plant science·2026
Same author

What impact do new homologs have on detecting interdomain horizontal gene transfer in eukaryotes? A reassessment of Katz (2015).

Biology open·2026

Related Experiment Video

Updated: Jan 20, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K

CompScore: Boosting Structure-Based Virtual Screening Performance by Incorporating Docking Scoring Function

Yunierkis Perez-Castillo1, Stellamaris Sotomayor-Burneo2, Karina Jimenes-Vargas3

  • 1Bio-Cheminformatics Research Group and Escuela de Ciencias Físicas y Matemáticas , Universidad de Las Americas , Quito 170504 , Ecuador.

Journal of Chemical Information and Modeling
|August 27, 2019
PubMed
Summary

This study introduces CompScore, a novel method for structure-based virtual screening (VS) that optimizes consensus scoring by selecting the best scoring function components. CompScore significantly enhances VS enrichment performance for drug discovery.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K
Screening Traditional Chinese Medicine Compounds for Inhibiting UCHL3 Activity Based on Molecular Docking and Deubiquitinating Enzyme Probe Technology
10:25

Screening Traditional Chinese Medicine Compounds for Inhibiting UCHL3 Activity Based on Molecular Docking and Deubiquitinating Enzyme Probe Technology

Published on: November 22, 2024

630

Related Experiment Videos

Last Updated: Jan 20, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

1.1K
A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

69.7K
Screening Traditional Chinese Medicine Compounds for Inhibiting UCHL3 Activity Based on Molecular Docking and Deubiquitinating Enzyme Probe Technology
10:25

Screening Traditional Chinese Medicine Compounds for Inhibiting UCHL3 Activity Based on Molecular Docking and Deubiquitinating Enzyme Probe Technology

Published on: November 22, 2024

630

Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Consensus scoring improves structure-based virtual screening (VS) performance over single functions.
  • The contribution of individual docking scoring function components in consensus scoring remains unanalyzed.

Purpose of the Study:

  • To develop and evaluate a method that integrates docking scoring function components into VS workflows.
  • To optimize consensus scoring by identifying the best combination of scoring components for enhanced VS enrichment.

Main Methods:

  • Implemented a method using genetic algorithms to find optimal combinations of scoring function components.
  • Validated the methodology using a dataset of 102 targets with known ligands and decoys.
  • Tested the method's predictive power on previously unseen external data.

Main Results:

  • The proposed method, CompScore, outperforms existing VS methods across all tested targets.
  • CompScore boosts enrichment performance by an average of 45% compared to traditional consensus scoring.
  • The method demonstrates robust predictive ability and retains performance even after redocking with different software.

Conclusions:

  • Optimizing consensus scoring by selecting specific scoring function components significantly enhances virtual screening efficiency.
  • CompScore offers a powerful and validated approach for improving hit identification in drug discovery.
  • The freely available CompScore tool facilitates advanced virtual screening applications.