Machine Learning Prediction of Electronic Coupling between the Guanine Bases of DNA

Charge
transport in deoxyribonucleic acid (DNA) is of immense interest
in biology and molecular electronics. Electronic coupling between
the DNA bases is an important parameter describing the efficiency
of charge transport in DNA. A reasonable estimation of this electronic
coupling requires many expensive first principle calculations. In
this article, we present a machine learning (ML) based model to calculate
the electronic coupling between the guanine bases of the DNA (in the
same strand) of any length, thus avoiding expensive first-principle
calculations. The electronic coupling between the bases are evaluated
using density functional theory (DFT) calculations with the morphologies
derived from fully atomistic molecular dynamics (MD) simulations.
A new and simple protocol based on the coarse-grained model of the
DNA has been used to extract the feature vectors for the DNA bases.
A deep neural network (NN) is trained with the feature vector as input
and the DFT-calculated electronic coupling as output. Once well trained,
the NN can predict the DFT-calculated electronic coupling of new structures
with a mean absolute error (MAE) of 0.02 eV.