Unsupervised Learning with Word Embeddings Captures Quiescent Knowledge from COVID-19 Drugs Literature

As COVID-19 patients flood hospitals worldwide, physicians are trying to search for effective antiviral therapies to save lives. However, there is currently a lack of proven effective medications against COVID-19. Multiple COVID-19 vaccine trials and treatments are underway, but yet need more time and testing. Furthermore, the SARS-CoV-2 virus that causes COVID-19 replicates poorly in multiple animals, including dogs, pigs, chickens, and ducks, which limits preclinical animal studies. We built an unsupervised deep learning model (CDVec) to produce word-embeddings using word2vec from a corpus of articles selectively focusing on COVID-19 candidate drugs that appeared in the literature to identify promising target drugs that could be used in COVID-19 treatment.