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

The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
Data Validation01:15

Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:

You might also read

Related Articles

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

Sort by
Same author

AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models.

mAbs·2026
Same author

Improvement of Peak Integration in Capillary Electrophoresis: Reference Data Set No. 1.

Electrophoresis·2026
Same author

Chemoinformatic regression methods and their applicability domain.

Molecular informatics·2024
Same author

Experimentally Observed Conformational Changes in Antibodies Due to Binding and Paratope-epitope Asymmetries.

Journal of pharmaceutical sciences·2023
Same author

Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation.

Journal of cheminformatics·2023
Same author

Evaluating High-Variance Leaves as Uncertainty Measure for Random Forest Regression.

Molecules (Basel, Switzerland)·2021
Same journal

PSDTA: An Approach to Drug-Target Binding Affinity Prediction by Integrating Physicochemical and Structural Information to Reduce Feature Redundancy.

Journal of chemical information and modeling·2026
Same journal

M-JEPA: Predictive Self-Supervised Learning for Molecular Graphs with Scaffold-Shift Evaluation on Tox21.

Journal of chemical information and modeling·2026
Same journal

Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.

Journal of chemical information and modeling·2026
Same journal

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026
Same journal

Intricate Role of Cholesterol in Membrane Fusion.

Journal of chemical information and modeling·2026
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

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

Related Experiment Video

Updated: Jun 23, 2026

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

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

Sebastian G Rohrer1, Knut Baumann

  • 1Institute of Pharmaceutical Chemistry, Beethovenstrasse 55, Braunschweig University of Technology, 38106 Braunschweig, Germany.

Journal of Chemical Information and Modeling
|May 13, 2009
PubMed
Summary
This summary is machine-generated.

Refined nearest neighbor analysis creates unbiased benchmark data sets for virtual screening. This method purges unselective hits, ensuring accurate validation of screening techniques.

Related Experiment Videos

Last Updated: Jun 23, 2026

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

Area of Science:

  • Computational chemistry
  • Spatial statistics
  • Bioinformatics

Background:

  • Virtual screening requires high-quality benchmark data sets for reliable method validation.
  • Existing methods may suffer from biases like analogue bias and artificial enrichment.
  • Refined nearest neighbor analysis offers a novel approach for analyzing spatial patterns.

Purpose of the Study:

  • To design unbiased benchmark data sets for virtual screening using refined nearest neighbor analysis.
  • To develop a workflow for purging irrelevant compounds and optimizing data set composition.
  • To enable Maximum Unbiased Validation (MUV) of virtual screening methodologies.

Main Methods:

  • Application of refined nearest neighbor analysis to PubChem bioactivity data.
  • Development of a workflow to remove unselective hits from screening data.
  • Utilizing topological optimization and experimental design for data set generation.
  • Ensuring data sets are unbiased regarding analogue bias and artificial enrichment.

Main Results:

  • Successfully generated benchmark data sets for virtual screening.
  • The created data sets are unbiased and suitable for Maximum Unbiased Validation (MUV).
  • A software package implementing the MUV design workflow is provided.

Conclusions:

  • Refined nearest neighbor analysis is effective for creating high-quality, unbiased virtual screening benchmark data sets.
  • The MUV design workflow enhances the reliability of virtual screening method evaluation.
  • Freely available data sets and software facilitate advancements in drug discovery research.