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

Abstract

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.