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Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

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Related Experiment Video

Updated: Jun 9, 2026

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop
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SKATE: a docking program that decouples systematic sampling from scoring.

Jianwen A Feng1, Garland R Marshall

  • 1Department of Biochemistry and Molecular Biophysics, Center for Computational Biology, Washington University School of Medicine, 660 S. Euclid Ave, St. Louis, Missouri 63110, USA.

Journal of Computational Chemistry
|August 27, 2010
PubMed
Summary

SKATE, a novel molecular docking prototype, decouples sampling from scoring for improved accuracy. This approach systematically explores ligand poses, achieving high success rates in redocking studies.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Molecular docking is crucial for predicting ligand-receptor interactions.
  • Current docking methods often couple sampling and scoring, potentially limiting accuracy.
  • Efficient and accurate pose prediction is essential for virtual screening and drug design.

Purpose of the Study:

  • To introduce SKATE, a novel docking prototype that separates systematic sampling from scoring.
  • To enhance molecular docking accuracy by decoupling these two critical processes.
  • To evaluate the performance of SKATE in redocking and cross-docking scenarios.

Main Methods:

  • SKATE employs systematic sampling of ligand conformational, rotational, and translational degrees of freedom.
  • Efficient sampling is achieved through techniques like aggregate assembly, discriminant analysis, adaptive sampling, radial sampling, and clustering.
  • The decoupling allows poses to be ranked by any scoring function.

Main Results:

  • SKATE achieved high accuracy in redocking studies, with 98%, 95%, and 98% of poses within 2 Å RMSD of the native structure for the Astex/CDCC, Surflex, and Vertex sets, respectively.
  • Cross-docking assessments on thymidine kinase and cyclin-dependent kinase also demonstrated SKATE's effectiveness.
  • The method successfully sampled sterically allowed poses by constraining ligand movement within the receptor pocket.

Conclusions:

  • SKATE's novel approach of decoupling sampling from scoring significantly improves docking accuracy.
  • The systematic sampling strategy effectively explores the ligand's conformational space.
  • SKATE represents a promising tool for enhancing molecular docking performance in drug discovery research.