Title: COVID-19 drugs invention using deep neural network models: an artificial intelligence approach

Authors: Pamir Roy; S.K. Tamang

Addresses: Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh-791109, India ' Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh-791109, India

Abstract: The Covid-19 disease caused by the novel Corona Virus (SARS-2) spread like a wildfire and scientists across the whole world have been trying to find a cure for the disease and as such resorted to all methods available. The tool of artificial intelligence (AI) and data science has proven very useful in this regard for rapid drug invention and development. In this paper, tending along the same line, four different deep neural networks (DNNs) based models (bi-directional long short-term memory (LSTM) with attention, constrained graph variational autoencoders (CGVAE), edge memory neural network (EENN) and connectivity map (CMAP) based DNN have been proposed for usage in drug Invention of highly effective lead molecules for the disease COVID-19. The models have been evaluated and performed well with the highest performance given by the bi-directional LSTM model with validity of 98.7%, uniqueness of 99.8% and originality of 97.4%.

Keywords: DNN; deep neural network; bi-directional LSTM with attention; CGVAE; constrained graph variational autoencoders; EENN; edge memory neural network; CMAP DNN; Covid-19 Drug Invention.

DOI: 10.1504/IJIEI.2021.117058

International Journal of Intelligent Engineering Informatics, 2021 Vol.9 No.2, pp.176 - 192

Received: 10 Nov 2020
Accepted: 13 Feb 2021
Published online: 13 Aug 2021 *

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