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

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 and 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 and computational resources. In this article, we present a machine learning (ML)-based model to calculate the electronic coupling between any two bases of dsDNA/dsRNA 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 neural network (NN) model can predict the electronic couplings between dsDNA base pairs with any structural orientation with a mean absolute error (MAE) of less than 0.014 eV. We further use the NN-predicted electronic coupling values to compute the dsDNA/dsRNA conductance.

journal_name

J Chem Inf Model

authors

Aggarwal A,Vinayak V,Bag S,Bhattacharyya C,Waghmare UV,Maiti PK

doi

10.1021/acs.jcim.0c01072

subject

Has Abstract

pub_date

2021-01-25 00:00:00

pages

106-114

issue

1

eissn

1549-9596

issn

1549-960X

journal_volume

61

pub_type

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