COVID-19 drugs invention using deep neural network models: an artificial intelligence approach. (30th July 2021)

• Record Type: Journal Article
• Authors: Roy, Pamir; Tamang, S.K.
• Is Part Of: International journal of intelligent engineering informatics. Volume 9:Number 2(2021)
• Page Start: 176
• Page End: 192
• Publication Date: 2021-07-30
• Subjects: 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; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

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%.