Molecular Dynamics Simulation & Deep Learning Integration for Visualizing Bax/Bak-Mediated Mitochondrial Outer Membrane Permeabilization (MOMP)

**Abstract:** The visualization of Bax/Bak-mediated Mitochondrial Outer Membrane Permeabilization (MOMP) remains a substantial challenge in cell death research. Current experimental techniques often provide limited resolution or indirect insights into the intricate molecular mechanisms underlying this process. This paper introduces a novel approach integrating high-throughput molecular dynamics (MD) simulations with advanced deep learning (DL) techniques to generate a comprehensive, dynamic visualization of MOMP. By leveraging existing MD simulation algorithms combined with novel DL architectures for feature extraction and visualization, we achieve significantly improved resolution and clarity compared to conventional methods. This offers immediate commercial value for drug discovery, fundamental cell biology research, and personalized medicine applications.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-02
DOI
https://doi.org/10.5281/zenodo.17247352
Primary Topic
Educational Methods and Analysis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Molecular Dynamics Simulation & Deep Learning Integration for Visualizing Bax/Bak-Mediated Mitochondrial Outer Membrane Permeabilization (MOMP)

Zenodo (CERN European Organization for Nuclear Research)
Educational Methods and Analysis
article

Molecular Dynamics Simulation & Deep Learning Integration for Visualizing Bax/Bak-Mediated Mitochondrial Outer Membrane Permeabilization (MOMP)

article en

Abstract

**Abstract:** The visualization of Bax/Bak-mediated Mitochondrial Outer Membrane Permeabilization (MOMP) remains a substantial challenge in cell death research. Current experimental techniques often provide limited resolution or indirect insights into the intricate molecular mechanisms underlying this process. This paper introduces a novel approach integrating high-throughput molecular dynamics (MD) simulations with advanced deep learning (DL) techniques to generate a comprehensive, dynamic visualization of MOMP. By leveraging existing MD simulation algorithms combined with novel DL architectures for feature extraction and visualization, we achieve significantly improved resolution and clarity compared to conventional methods. This offers immediate commercial value for drug discovery, fundamental cell biology research, and personalized medicine applications.

Zenodo (CERN European Organization for Nuclear Research)
Freed–Hardeman University (US)
Good health and well-being
Openalex Percentile: Top 35%
Educational Methods and Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.