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

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...

MO Theory and Covalent Bonding

MO Theory and Covalent Bonding

The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...

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.

Fischer Projections

Fischer Projections

Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Orbital Theory II

Molecular Orbital Theory II

Molecular Orbital Energy Diagrams

You might also read

Related Articles

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

Sort by

Same author

Elevated medial prefrontal GABA/Glx ratio in preschool autistic children: A <sup>1</sup>H-MRS study of cognitive and behavioral correlates.

Progress in neuro-psychopharmacology & biological psychiatry·2026

Same author

Deciphering tumor metabolites: emerging technologies shaping clinical implications.

Trends in cancer·2026

Same author

Impact of screen time on white matter integrity and neurodevelopment in children with autism spectrum disorder: a diffusional kurtosis imaging mediation analysis.

European child & adolescent psychiatry·2026

Same author

Adaptive Niching-Based Gradient-Accelerated Differential Evolution for High-Dimensional Nonconvex Optimization.

IEEE transactions on neural networks and learning systems·2026

Same author

Alterations in white matter connectivity of the dorsal and ventral language pathways in children with autism spectrum disorder.

Psychiatry research·2025

Same author

Associations between vitamin D and brain volume in children with autism spectrum disorder.

Pediatric research·2025

Search research articles

Related Experiment Video

Updated: Jul 7, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

Factorization in molecular modeling and belief propagation algorithms.

Bochuan Du¹, Pu Tian^1,2

¹School of Life Sciences, Jilin University, Changchun 130012, China.

Mathematical Biosciences and Engineering : MBE

|December 21, 2023

Summary

This summary is machine-generated.

Factorization techniques simplify complex systems in machine learning and molecular modeling. This work explores connections between these methods to advance physical modeling of molecular systems.

Keywords:

belief propagation gaussian belief propagation local distribution theory loopy belief propagation molecular simulation neural network repetitive local sampling repetitive message passing

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

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Related Experiment Videos

Last Updated: Jul 7, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

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

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

Area of Science:

Computational physics
Statistical machine learning
Computational chemistry

Background:

Factorization is crucial for reducing computational complexity in high-dimensional systems.
Molecular modeling uses approximate factorization for interactions, while machine learning employs belief propagation algorithms.
These fields have developed factorization methods independently.

Purpose of the Study:

To bridge the gap between factorization algorithms in molecular modeling and machine learning.
To highlight the common foundation of probability distribution factorization.
To encourage cross-disciplinary development of advanced factorization techniques.

Main Methods:

Review of conventional molecular modeling techniques (e.g., molecular dynamics, Monte Carlo).
Introduction of local distribution theory for factorizing molecular system distributions.
Discussion of belief propagation and loopy belief propagation algorithms.

Main Results:

Identified factorization of probability distributions as a unifying concept.
Presented connections and differences between molecular modeling and machine learning factorization approaches.
Highlighted the independent development trajectories of these algorithms.

Conclusions:

Further development of factorization algorithms can benefit physical modeling of complex molecular systems.
Cross-pollination of ideas between machine learning and molecular modeling is encouraged.
This perspective aims to stimulate novel research at the intersection of these fields.