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 Experiment Video

Updated: Nov 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.3K

Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model.

Abhishek Aggarwal1, Vinayak Vinayak2, Saientan Bag1

  • 1Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India.

Journal of Chemical Information and Modeling
|December 15, 2020
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

IntravChip: a vascularized and perfused microfluidic model of the primary tumor microenvironment to collect intravasated tumor cells.

Biofabrication·2026
Same author

Multicentered <i>p-s-p*</i> Antibonding Interaction Induces Strong Anharmonicity and High Thermoelectric Performance in Complex Metal Chalcohalide Tl<b><sub>5</sub></b>Te<b><sub>2</sub></b>I.

Journal of the American Chemical Society·2026
Same author

Entropy, Free Energy, and a Generalized Order Parameter for Liquid Crystal Phases of Chiral and Achiral Rods.

Journal of chemical theory and computation·2026
Same author

Memory control of ice growth during non-equilibrium freezing of water.

The Journal of chemical physics·2026
Same author

Towards a context-aware framework for cellular senescence.

Biogerontology·2026
Same author

Donor-Acceptor Stacks in Flexible Porous Coordination Polymers Toward Room-Temperature Ferroelectricity.

Angewandte Chemie (International ed. in English)·2026
This summary is machine-generated.

This study introduces a machine learning model to quickly calculate electronic couplings in double-stranded DNA (dsDNA). This method bypasses slow computations, enabling faster research in molecular electronics and DNA/RNA studies.

Area of Science:

  • Molecular electronics
  • Biophysics
  • Computational chemistry

Background:

  • Double-stranded DNA (dsDNA) facilitates charge migration, crucial for molecular electronics and biological research.
  • Charge migration rates depend on electronic couplings between DNA/RNA nucleobases, which are sensitive to geometry.
  • First-principles calculations for these couplings are computationally intensive.

Purpose of the Study:

  • To develop a machine learning (ML) model for predicting electronic couplings between dsDNA/dsRNA base pairs.
  • To bypass computationally expensive first-principles calculations for estimating electronic couplings.
  • To enable efficient analysis of charge transport in DNA.

Main Methods:

  • Utilized a Coulomb matrix representation encoding atomic identities and coordinates of DNA base pairs.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Related Experiment Videos

Last Updated: Nov 25, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.3K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K
  • Trained a feedforward neural network (NN) model using the prepared dataset.
  • Applied the NN model to predict electronic couplings for various base pair orientations.

    • Main Results:

    • The NN model accurately predicts electronic couplings between dsDNA base pairs with a mean absolute error (MAE) below 0.014 eV.
    • The model effectively handles diverse structural orientations of base pairs.
    • Predicted couplings were used to compute dsDNA/dsRNA conductance.

    Conclusions:

    • The ML-based approach provides a computationally efficient alternative to first-principles methods for calculating electronic couplings in dsDNA/dsRNA.
    • This model accelerates the study of charge migration in DNA and its applications in molecular electronics.
    • The developed NN model offers a valuable tool for understanding structure-property relationships in nucleic acids.