  Researchers worldwide are seeking to repurpose existing drugs or discover new
drugs to counter the disease caused by severe acute respiratory syndrome
coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies
is molecules that have been reported in the scientific literature to be
drug-like in the context of coronavirus research. We report here on a project
that leverages both human and artificial intelligence to detect references to
drug-like molecules in free text. We engage non-expert humans to create a
corpus of labeled text, use this labeled corpus to train a named entity
recognition model, and employ the trained model to extract 10912 drug-like
molecules from the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of
198875 papers. Performance analyses show that our automated extraction model
can achieve performance on par with that of non-expert humans.
