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.

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

You might also read

Related Articles

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

Sort by

Same author

Lenacapavir-induced lattice hyperstabilization is central to HIV-1 capsid failure at the nuclear pore complex and in the cytoplasm.

eLife·2026

Same author

Systematic bottom-up coarse-graining of hydrated excess proton transport across scales.

Nature computational science·2026

Same author

Mechanism of HIV-1 Capsid Rupture and Uncoating by Reverse Transcription.

bioRxiv : the preprint server for biology·2026

Same author

Physical Confinement Modulates the Rate-Limiting Transition in the Release of Phosphate from Actin Filaments.

bioRxiv : the preprint server for biology·2026

Same author

Hydration-Controlled Proton Transport in Respiratory Complex I.

Journal of the American Chemical Society·2026

Same author

Mechanistic insights into lenacapavir-induced off-pathway HIV-1 capsid assembly.

Proceedings of the National Academy of Sciences of the United States of America·2026

Search research articles

Related Experiment Video

Updated: May 14, 2025

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

A Hybrid Bottom-Up and Data-Driven Machine Learning Approach for Accurate Coarse-Graining of Large Molecular

Korbinian Liebl¹, Gregory A Voth¹

¹Department of Chemistry, Chicago Center for Theoretical Chemistry, Institute for Biophysical Dynamics, and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, United States.

Journal of Chemical Theory and Computation

|April 17, 2025

Summary

This study introduces a hybrid coarse-graining method using machine learning to improve molecular simulations. The new approach accurately predicts binding affinity and complex structures, overcoming limitations of traditional methods.

More Related Videos

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Related Experiment Videos

Last Updated: May 14, 2025

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

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Computational chemistry
Molecular dynamics
Biophysics

Background:

Bottom-up coarse-graining develops low-resolution models from atomistic simulations.
Force-matching and relative entropy minimization are common but can overfit limited atomistic data.
Overfitting is problematic for large molecular complexes, affecting binding affinity prediction.

Purpose of the Study:

To develop a data-driven machine learning hybrid coarse-graining approach.
To regularize the relative entropy minimization method for improved model accuracy.
To create coarse-grained models that accurately predict binding affinity and complex structure.

Main Methods:

A novel hybrid coarse-graining concept based on regularized relative entropy minimization.
Utilizing machine learning for data-driven model development.
Validation against atomistic simulation data for molecular complexes.

Main Results:

The developed models accurately reproduce targeted binding affinities.
The models describe the underlying complex structures with high fidelity.
Trained models exhibit diverse behavior, including frequent binding/unbinding events.

Conclusions:

The hybrid coarse-graining approach overcomes limitations of traditional methods.
This method enables accurate simulation of molecular complex interactions and structures.
The models are transferable for simulating larger systems like protein lattices and viral capsids.