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

Sampling Methods: Overview01:06

Sampling Methods: Overview

2.3K
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...
2.3K
Sampling Theorem01:15

Sampling Theorem

1.3K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.3K
Upsampling01:22

Upsampling

575
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
575
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

2.2K
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
2.2K
Aliasing01:18

Aliasing

562
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
562
Convenience Sampling Method00:55

Convenience Sampling Method

10.9K
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.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
10.9K

You might also read

Related Articles

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

Sort by
Same author

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Journal of chemical information and modeling·2026
Same author

AI decodes protein-ligand binding.

Nature chemical biology·2026
Same author

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Nature protocols·2026
Same author

EpiMII: Structure-Aware Graph Neural Networks for MHC-II Epitope Generation.

Research (Washington, D.C.)·2026
Same author

Overcoming Resistance in the Androgen Receptor: Rational and Strategic Design of Advanced Antagonists.

Accounts of chemical research·2026
Same author

Targeting the intrinsically disordered AR-NTD through a machine learning-based enhanced sampling workflow.

Nature communications·2026

Related Experiment Video

Updated: Jan 14, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K

Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.

Kai Zhu1, Enrico Trizio2, Jintu Zhang1

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.

Chemical Reviews
|October 22, 2025
PubMed
Summary

Machine learning enhances molecular dynamics simulations by improving rare-event sampling. This data-driven approach aids in understanding complex systems and accelerates scientific discovery.

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Related Experiment Videos

Last Updated: Jan 14, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.5K

Area of Science:

  • Computational chemistry and biophysics
  • Interdisciplinary applications of machine learning

Background:

  • Molecular dynamics (MD) simulations offer insights into microscopic behavior but are limited by long timescales for rare events.
  • Enhanced sampling methods are crucial for overcoming these timescale limitations in MD simulations.

Purpose of the Study:

  • To provide a comprehensive review of machine learning integration in enhanced sampling methods for molecular dynamics.
  • To highlight the impact of these integrated techniques on various scientific applications.

Main Methods:

  • Focus on data-driven construction of collective variables using machine learning.
  • Exploration of improved biasing schemes through machine learning.
  • Investigation of novel strategies like reinforcement learning and generative approaches.

Main Results:

  • Machine learning significantly improves the efficiency of rare-event sampling in molecular dynamics.
  • Data-driven collective variable construction is a key advancement.
  • Reinforcement learning and generative models offer new avenues for enhanced sampling.

Conclusions:

  • Machine learning and enhanced sampling methods are revolutionizing molecular dynamics simulations.
  • Future directions focus on automating rare-event sampling strategies.
  • The integration accelerates research in biomolecular processes, ligand binding, and materials science.