Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

You might also read

Related Articles

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

Sort by

Same author

Molecular Mechanisms of KIT Receptor Dimerization and Oncogenic Activation Revealed by Multiscale Simulations.

Journal of chemical information and modeling·2026

Same author

Enhanced Condensation of RNA Repeats Induced by Terahertz Oscillatory Fields.

Molecules (Basel, Switzerland)·2026

Same author

Correction to "Efficient Protein-Ligand Binding Free Energy Estimation with Coarse-Grained Funnel Metadynamics".

Journal of chemical theory and computation·2026

Same author

Coarse-Grained Simulations Reveal Salt- and Length-Dependent Condensation of G4C2 RNA Repeats.

The journal of physical chemistry letters·2026

Same author

Martini 3 Metabolome.

Journal of chemical theory and computation·2026

Same author

Structural basis of signal peptide recognition by the signal peptidase complex.

Nature communications·2026

Same journal

DeepDPM: A Deep Learning Method for MoRFs Prediction Based on Wavelet Transform and Dynamic Convolutional Attention Mechanism.

Journal of chemical information and modeling·2026

Same journal

Graph-Based Generation and Reduction of Complex Chemical Reaction Networks.

Journal of chemical information and modeling·2026

Same journal

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition.

Journal of chemical information and modeling·2026

Same journal

Machine Learning-Driven Discovery of Indole/Oxoindole-Piperazine Scaffolds as Dual MAO-B/Sig-1R Ligands for Neurodegenerative Disorders.

Journal of chemical information and modeling·2026

Same journal

Mapping Evolution of Molecules across Biochemistry with Assembly Theory.

Journal of chemical information and modeling·2026

Same journal

Structural Proteomics-Based Deciphering of Hydrophobic Packing Fingerprints Informing Protein Thermostability in TIM Barrels.

Journal of chemical information and modeling·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 13, 2025

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

Facilitating CG Simulations with MAD: The MArtini Database Server.

Cécile Hilpert¹, Louis Beranger¹, Paulo C T Souza¹

¹Microbiologie Moléculaire et Biochimie Structurale (MMSB), UMR 5086 CNRS & University of Lyon. 7 passage du Vercors, 69367 Lyon, France.

Journal of Chemical Information and Modeling

|January 19, 2023

Summary

This summary is machine-generated.

The MArtini Database (MAD) is a web server for sharing Martini coarse-grained (CG) force field models. It converts atomistic structures to CG models and prepares complex systems for molecular dynamics simulations.

More Related Videos

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Aug 13, 2025

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Author Spotlight: Streamlining Visual Dynamics to Simplify Molecular Dynamics Simulations Using Gromacs

Published on: August 9, 2024

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Novel 3D/VR Interactive Environment for MD Simulations, Visualization and Analysis

Published on: December 18, 2014

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

Computational chemistry
Biophysics
Materials science

Background:

The Martini force field is widely used for coarse-grained molecular dynamics (MD) simulations.
Sharing molecular structures and topologies is crucial for reproducible research.
Generating input files for complex systems can be time-consuming.

Purpose of the Study:

To present the MArtini Database (MAD) web server.
To facilitate the use of the Martini 3 coarse-grained force field.
To streamline the preparation of molecular systems for MD simulations.

Main Methods:

The MAD server provides tools for submitting and retrieving coarse-grained (CG) models.
It converts atomistic structures to CG representations with user-defined control.
It assembles biomolecules into large systems for simulation.

Main Results:

MAD supports a wide range of molecules including lipids, carbohydrates, and nanoparticles.
The server generates input files compatible with the GROMACS MD engine.
It specifically supports the latest Martini 3 force field.

Conclusions:

MAD simplifies the creation and sharing of Martini CG models.
The server accelerates the setup of complex molecular systems for MD simulations.
MAD enhances the accessibility and application of coarse-grained modeling.