Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model

Aggarwal, A and Vinayak, V and Bag, S and Bhattacharyya, C and Waghmare, UV and Maiti, PK (2021) Predicting the DNA Conductance Using a Deep Feedforward Neural Network Model. In: Journal of Chemical Information and Modeling, 61 . pp. 106-114.

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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. © 2020 American Chemical Society.

Item Type:	Journal Article
Publication:	Journal of Chemical Information and Modeling
Publisher:	American Chemical Society
Additional Information:	The copyright of this article belongs to American Chemical Society
Keywords:	Calculations; Couplings; Deep neural networks; DNA; Turing machines, Biological research; Computational resources; Double-stranded DNA (ds-DNA); Electronic coupling; First-principles calculation; Mean absolute error; Neural network model; Structural orientations, Feedforward neural networks
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation UG Programme Division of Physical & Mathematical Sciences > Physics
Date Deposited:	17 Feb 2021 08:34
Last Modified:	17 Feb 2021 08:34
URI:	http://eprints.iisc.ac.in/id/eprint/67691

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