Predicting the DNA Conductance using Deep Feed Forward Neural Network Model

TL;DR

This paper introduces a deep neural network model that accurately predicts electronic couplings in DNA/RNA, enabling efficient conductance estimation without costly first-principles calculations.

Contribution

The study develops a machine learning approach using Coulomb matrix representations to predict DNA base electronic couplings, significantly reducing computational effort.

Findings

Neural network predicts electronic couplings with MAE < 0.014 eV.

Model accurately estimates DNA conductance across various geometries.

Reduces computational time compared to first-principles methods.

Abstract

Double-stranded DNA (dsDNA) has been established as an efficient medium for charge migration, bringing it to the forefront of the field of molecular electronics as well as biological research. The charge migration rate is controlled by the electronic couplings between the two nucleobases of DNA/RNA. These electronic couplings strongly depend on the intermolecular geometry and orientation. Estimating these electronic couplings for all the possible relative geometries of molecules using the computationally demanding first-principles calculations requires a lot of time as well as computation resources. In this article, we present a Machine Learning (ML) based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA of any length and sequence and bypass the computationally expensive first-principles calculations. Using the Coulomb matrix representation which encodes the atomic identities and coordinates of the DNA base pairs to prepare the input dataset, we train a feedforward neural network model. Our NN model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a MAE of less than 0.014 eV. We further use the NN predicted electronic coupling values to compute the dsDNA/dsRNA conductance.