A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning

Abstract

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around the globe. Despite of 141,975 published papers on COVID-19 and several hundreds of new studies carried out every day, this pandemic remains as a global challenge. Biomedical literature mining helps the researchers to understand the etiology of the disease and to gain an in–depth knowledge of the disease, potential drugs, vaccines developed and novel therapies. In addition to the available treatments, there is a huge need to address the comorbidity-based disease mortality in case of COVID-19 patients with type 2 diabetes mellitus (T2D), hypertension and cardiovascular disease (CVD). In this chapter, we provide a hybrid protocol based on biomedical literature mining, network analysis of omics data, and deep learning for the identification of most potential drugs for COVID-19.