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

Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Entropy and Solvation02:05

Entropy and Solvation

The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ ≥ 15); an...
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

You might also read

Related Articles

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

Sort by
Same author

DEL2PH4: Predictive 3D Pharmacophores from DNA-Encoded Library Screening Data.

ACS medicinal chemistry letters·2026
Same author

A Pharmacophore-Based Method for Rapid and Accurate Virtual Screening of Antibody Libraries against Antigens.

Molecular pharmaceutics·2025
Same author

Improved Accuracy for Modeling PROTAC-Mediated Ternary Complex Formation and Targeted Protein Degradation <i>via</i> New <i>In Silico</i> Methodologies.

Journal of chemical information and modeling·2020
Same author

AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets.

Journal of chemical information and modeling·2020
Same author

Fragment Hits: What do They Look Like and How do They Bind?

Journal of medicinal chemistry·2019
Same author

In Silico Modeling of PROTAC-Mediated Ternary Complexes: Validation and Application.

Journal of chemical information and modeling·2019
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
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

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

Related Experiment Video

Updated: May 24, 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

Numerical errors and chaotic behavior in docking simulations.

Miklos Feher1, Christopher I Williams

  • 1Campbell Family Institute for Breast Cancer Research, University Health Network, Toronto Medical Discovery Tower, 101 College Street, Suite 5-361, Toronto, ON, M5G 1L7, Canada. mfeher@uhnres.utoronto.ca

Journal of Chemical Information and Modeling
|March 3, 2012
PubMed
Summary
This summary is machine-generated.

Molecular docking programs show high sensitivity to minor changes in ligand input files, leading to significantly different predicted binding poses and scores. This sensitivity impacts virtual screening and drug discovery efforts.

More Related Videos

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Related Experiment Videos

Last Updated: May 24, 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

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English
14:34

A Bilingual Computational Workflow for Identifying Potential PLK1 Inhibitors in American Sign Language and English

Published on: April 3, 2026

Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Drug discovery

Background:

  • Molecular docking is a key computational technique for predicting ligand-protein interactions.
  • Accurate pose prediction and scoring are crucial for virtual screening and drug design.
  • Current docking algorithms are assumed to be robust to small variations in input data.

Purpose of the Study:

  • To investigate the sensitivity of molecular docking programs to subtle alterations in ligand input files.
  • To quantify the impact of input file variations on predicted docked poses and scores.
  • To differentiate between variations caused by algorithmic sensitivity and those due to conformational searching limitations.

Main Methods:

  • Systematic analysis of docking program output with minor perturbations to ligand input structures.
  • Statistical characterization of docking pose and score variations.
  • Comparison of sensitivity between high-throughput and precise docking methods.

Main Results:

  • Nearly identical ligand structures can yield dramatically different top-scoring poses and binding modes.
  • Even changes in atom order within input files significantly alter docking outcomes.
  • Docking variations are partly attributable to numerical sensitivity and chaotic effects within algorithms.

Conclusions:

  • Current molecular docking algorithms exhibit significant sensitivity to input file variations.
  • This sensitivity can lead to unreliable pose prediction, ranking, and virtual screening results.
  • Re-evaluation of docking methodologies and input data handling is warranted for improved accuracy.