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

Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

15.5K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
15.5K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.8K
3.8K
Regression Analysis01:11

Regression Analysis

8.9K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.9K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

5.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
5.2K
Multiple Regression01:25

Multiple Regression

4.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Catalytic Asymmetric Hydration of Alkenes.

Journal of the American Chemical Society·2026
Same author

Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks.

ACS central science·2026
Same author

Current Insights on Skin Permeability Data and Quantitative Structure-Property Relationship Modeling.

Molecular informatics·2026
Same author

Interpretable and Scalable Similarity Metrics for DNA-Encoded Library Design Using Generative Topographic Mapping.

Molecular informatics·2026
Same author

Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability.

Journal of chemical theory and computation·2026
Same author

Peptidomimetics Inspired by α-Synuclein or Its Chaperone αB-Crystallin Differentially Modulate α-Synuclein Aggregation.

Journal of medicinal chemistry·2026
Same journal

Correction to "AstraMEV (AI-Guided Structural Assembly of Multi-Epitope Vaccines) Against Infectious Bronchitis Virus".

Journal of chemical information and modeling·2026
Same journal

MolPy: A Large Language Model-Friendly Toolkit for Reactive Topology Editing in Polymer Simulations.

Journal of chemical information and modeling·2026
Same journal

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026
Same journal

Structural and Thermodynamic Discrimination between Agonists and Antagonists of Retinoic Acid Receptor γ and the Vitamin D Receptor.

Journal of chemical information and modeling·2026
Same journal

PACEff Builder: An Efficient Platform for Constructing PACE Hybrid-Resolution Models for Molecular Dynamics Simulations of Aqueous Protein, Peptide Assembly, and Membrane Protein Systems.

Journal of chemical information and modeling·2026
Same journal

TransKla: A Local-Global Cross-Attention Based Transformer Approach for Prediction of Lysine Lactylation Sites.

Journal of chemical information and modeling·2026
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

Kernel Target Alignment Parameter: A New Modelability Measure for Regression Tasks.

Gilles Marcou1, Dragos Horvath1, Alexandre Varnek1,2

  • 1Laboratory of Chemoinformatics, University of Strasbourg , 1 rue Blaise Pascal, 67000 Strasbourg, France.

Journal of Chemical Information and Modeling
|December 18, 2015
PubMed
Summary
This summary is machine-generated.

Kernel Target Alignment (KTA) effectively predicts molecular descriptor relevance for Quantitative Structure-Activity Relationship (QSAR) modeling. Combining KTA with other measures like Div and Sim offers a robust QSAR modelability assessment.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform
08:10

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Published on: October 6, 2019

7.0K

Related Experiment Videos

Last Updated: Mar 28, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform
08:10

Fabrication and Implementation of a Reference-Free Traction Force Microscopy Platform

Published on: October 6, 2019

7.0K

Area of Science:

  • Computational Chemistry
  • Cheminformatics
  • Drug Discovery

Background:

  • Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for drug discovery.
  • Selecting relevant molecular descriptors is a key challenge in QSAR.
  • Modelability assessment helps predict the success of QSAR models.

Purpose of the Study:

  • To evaluate Kernel Target Alignment (KTA) as a modelability measure for QSAR.
  • To compare KTA with other modelability metrics (Div, Sim).
  • To develop a consensus index for improved QSAR modelability assessment.

Main Methods:

  • QSAR modeling studies using 25 GPCR binder datasets and a toxicity dataset.
  • Employing over 100 ISIDA fragment descriptors and ChemAxon BCUT terms.
  • Benchmarking KTA against Jaccard distance (Div) and a 1-NN based measure (Sim).

Main Results:

  • KTA consistently anticorrelated with QSAR model performance (RMSE).
  • Div and Sim demonstrated performance comparable to KTA.
  • A consensus index integrating KTA, Div, and Sim showed a more robust correlation with RMSE.

Conclusions:

  • KTA is an efficient tool for estimating molecular descriptor relevance in QSAR.
  • A consensus approach combining KTA, Div, and Sim enhances QSAR modelability prediction.
  • This work provides valuable insights for optimizing descriptor selection in QSAR studies.