Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning

Abstract

There have been more than 2.2 million confirmed cases and over 120 000 deaths from the human coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), in the United States alone. However, there is currently a lack of proven effective medications against COVID-19. Drug repurposing offers a promising route for the development of prevention and treatment strategies for COVID-19. This study reports an integrative, network-based deep-learning methodology to identify repurposable drugs for COVID-19 (termed CoV-KGE). Specifically, we built a comprehensive knowledge graph that includes 15 million edges across 39 types of relationships connecting drugs, diseases, proteins/genes, pathways, and expression from a large scientific corpus of 24 million PubMed publications. Using Amazon’s AWS computing resources and a network-based, deep-learning framework, we identified 41 repurposable drugs (including dexamethasone, indomethacin, niclosamide, and toremifene) whose therapeutic associations with COVID-19 were validated by transcriptomic and proteomics data in SARS-CoV-2-infected human cells and data from ongoing clinical trials. Whereas this study by no means recommends specific drugs, it demonstrates a powerful deep-learning methodology to prioritize existing drugs for further investigation, which holds the potential to accelerate therapeutic development for COVID-19.

Artificial intelligence in COVID-19 drug repurposing

“…43 A further computational biophysics study 44 suggested that toremifene might block interaction between ACE2 and the spike protein of SARS-CoV-2 and might inhibit nonstructural protein 14 of SARS-CoV-2 (figure 1), mechanistically supporting the drug's antiviral activities. The mean plasma concentration of toremifene during administration of 60 mg per day was 0•88 mg/L (2•17 µM) in post-menopausal patients with breast cancer 40 and the peak plasma concentration (>10 µM) of toremifene (60 mg per day) was approximately three-times the antiviral effect on SARS-CoV-2 (half-maximal inhibitory 43 In summary, toremifene, identified by CoV-KGE 35 and network medi cine 16 approaches, offers a potential drug candidate to be tested in COVID-19 clinical trials.…”

“…47 Dexamethasone was identified as a top repur posed drug candidate by CoV-KGE. 35 The ran domised trial 48…”

“… 34 BenevolentAI predicted that baricitinib, a drug used to treat rheumatoid arthritis, could be a potential treatment for COVID-19 through the inhibition of AP2-associated protein kinase 1 (encoded by AAK1 ; figure 1 ). A team constructed a comprehensive COVID-19 knowledge graph (termed CoV-KGE) that included 15 million edges across 39 types of relationships connecting drugs, diseases, proteins, genes, pathways, and expressions of genes and proteins 35 from a large scientific corpus of 24 million PubMed publications. Using Amazon Web Services' computing resources and graph representation learning techniques, the team identified 41 repurposed drug candidates (including dexamethasone and melatonin) for COVID-19 treatment.…”

Potential Therapeutic Agents and Associated Bioassay Data for COVID-19 and Related Human Coronavirus Infections

“… 149 In addition, Zeng et al demonstrated that deep learning is a powerful methodology to prioritize existing drugs for further investigation. 150 Using a library of commercially available compounds, Elmezayen et al discovered several potential inhibitors against 3CLpro or TMPRSS2 with virtual screening and further evaluated their absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles. 151 It can be expected that computer-screening of compounds and modeling will be increasingly used in the discovery of drugs for COVID-19 and other viral infections to expedite the drug development process and lower its cost.…”

selfRL: Two-Level Self-Supervised Transformer Representation Learning for Link Prediction of Heterogeneous Biomedical Networks

“…Predicting potential links in heterogeneous biomedical networks (HBNs) can be beneficial to various significant biology and medicine problems, such as target identification, drug repositioning, and adverse drug reaction predictions. For example, network-based drug repositioning methods have already offered promising insights to boost the effective treatment of COVID-19 disease (Zeng et al 2020; Xiaoqi et al 2020), since it outbreak in December of 2019. Many network-based learning approaches have been developed to facilitate link prediction in HBNs.…”