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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
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...
88
Precipitate Formation and Particle Size Control01:16

Precipitate Formation and Particle Size Control

843
In precipitation gravimetry, the precipitating agent should react specifically or selectively with the analyte. While a specific reagent reacts with the analyte alone, a selective reagent can react with a limited number of chemical species.
The obtained precipitate should be either a pure substance of known composition or easily converted to one by a simple process, such as ignition or drying. In addition, the precipitate should be insoluble and easily filterable. In general, filterability...
843
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

307
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...
307

You might also read

Related Articles

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

Sort by
Same author

Molecular Interpretation of Temperature-Induced DNA Melting Monitored through Base Dipole Moment Changes as a Surrogate of the Hypochromic Shift.

The journal of physical chemistry letters·2026
Same author

Agonist-specific conformational dynamics at the β<sub>2</sub>-adrenoceptor dictate allosteric modulation of Gαs signalling and bronchodilation.

British journal of pharmacology·2026
Same author

LOX-A4 shapes Triticum urartu gene pools and contributions to the A subgenome of polyploid wheat.

Nature communications·2026
Same author

Targeting conserved domains of hypoxia-inducible factors for cancer therapy.

The Journal of experimental medicine·2026
Same author

Computational Modeling of PROTAC Ternary Complexes as Ensembles Using SILCS-xTAC.

Journal of chemical information and modeling·2025
Same author

Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG K<sup>+</sup>, Na<sub>v</sub>1.5, and Ca<sub>v</sub>1.2 Channels.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Aug 1, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K

GPU-specific algorithms for improved solute sampling in grand canonical Monte Carlo simulations.

Mingtian Zhao1, Abhishek A Kognole2, Sunhwan Jo2

  • 1Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland, USA.

Journal of Computational Chemistry
|April 24, 2023
PubMed
Summary

This study enhances molecular simulations by improving the efficiency of Grand Canonical Monte Carlo (GCMC) methods for larger molecules and complex systems. The new approach utilizes GPU architecture and system partitioning for faster, more accurate sampling in drug discovery and biophysics.

Keywords:
SILCSchemical potentialco-solvent molecular dynamicscomputer-aided drug designenhanced solute sampling

More Related Videos

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.9K
Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
12:03

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

6.2K

Related Experiment Videos

Last Updated: Aug 1, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.3K
Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
10:52

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

12.9K
Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil
12:03

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

6.2K

Area of Science:

  • Computational chemistry and molecular modeling.
  • Development of advanced simulation algorithms.

Background:

  • Grand Canonical Monte Carlo (GCMC) simulations are crucial for molecular simulations but struggle with sampling large molecules.
  • Existing GCMC methods are computationally demanding with low acceptance rates for insertions.

Purpose of the Study:

  • To enhance the sampling efficiency of GCMC simulations for complex molecular systems.
  • To adapt GCMC methods for GPU architecture to accelerate computations.

Main Methods:

  • Integration of cavity-bias and configurational-bias algorithms with GPU architecture (CUDA and OpenCL).
  • Extension of configurational-bias using system partitioning and random interval extraction for parallel processing.
  • Combination with an enhanced excess chemical potential (μex) oscillation protocol.

Main Results:

  • Achieved significant speed-up (~53-fold) in simulations of large systems like the BK Channel using system partitioning.
  • Demonstrated improved efficiency and suitability for GPU computing through parallelized sampling.
  • Successfully applied the enhanced method to site-identification by ligand competitive saturation (SILCS) for protein CDK2.

Conclusions:

  • The developed GCMC method significantly improves computational efficiency and sampling capabilities for large molecular systems.
  • The GPU-accelerated approach is well-suited for complex biological and chemical simulations.
  • This advancement facilitates applications in areas like drug discovery and understanding protein-ligand interactions.