@article{WOS:000613719400011,
 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.},
 author = {Aggarwal, Abhishek and Vinayak, Vinayak and Bag, Saientan and
Bhattacharyya, Chiranjib and Waghmare, V, Umesh and Maiti, Prabal K.},
 doi = {10.1021/acs.jcim.0c01072},
 eissn = {1549-960X},
 issn = {1549-9596},
 journal = {JOURNAL OF CHEMICAL INFORMATION AND MODELING},
 month = {JAN 25},
 number = {1},
 orcid-numbers = {Aggarwal, Abhishek/0000-0002-3342-6484
Maiti, Prabal K/0000-0002-9956-1136
Waghmare, Umesh/0000-0002-9378-155X},
 pages = {106-114},
 researcherid-numbers = {Aggarwal, Abhishek/ABB-1907-2020
},
 title = {Predicting the DNA Conductance Using a Deep Feedforward Neural Network
Model},
 unique-id = {WOS:000613719400011},
 volume = {61},
 year = {2021}
}