Dr. Manar Samad, a faculty member in TSU’s Department of Computer Science,has partnered with Dr. Sabber Ahamed (Asurion) to develop a graph-based model using abstracts of 10,683 scientific articles to find key information on three topics: transmission, drug types, and genome research related to the coronavirus. A subgraph is built for each of the three topics to extract more topic-focused information. The subgraph uses a measurement to rank order the importance of keywords related to drugs, diseases, pathogens, hosts of pathogens, and biomolecules. The results reveal intriguing information about antiviral drugs (Chloroquine, Amantadine, Dexamethasone), pathogen-hosts (pigs, bats, macaque, cynomolgus), viral pathogens (zika, dengue,malaria, and several viruses in the coronaviridae virus family), and proteins and therapeutic mechanisms (oligonucleotide, interferon, glycoprotein) in connection with the core topic of coronavirus. The categorical summary of these keywords and topics may be a useful reference to expedite and recommend new and alternative directions for COVID-19 research. Drs. Samad and Ahamed have submitted a paper to the Journal of Biomedical Informatics on their findings titled “Information Mining for COVID-19 Research From a Large Volume of Scientific Literature” in response to the White House Call to Action to the Tech Community on New Machine Readable COVID-19 Datasets. Requested by The White House Office of Science and Technology Policy, the dataset represents the most extensive machine-readable coronavirus literature collection available for data and text mining to date.