id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-203232-1nnqx1g9	Canturk, Semih	Machine-Learning Driven Drug Repurposing for COVID-19	2020-06-25		.txt	text/plain	5023	257	52	Using the National Center for Biotechnology Information virus protein database and the DrugVirus database, which provides a comprehensive report of broad-spectrum antiviral agents (BSAAs) and viruses they inhibit, we trained ANN models with virus protein sequences as inputs and antiviral agents deemed safe-in-humans as outputs. Using sequences for SARS-CoV-2 (the coronavirus that causes COVID-19) as inputs to the trained models produces outputs of tentative safe-in-human antiviral candidates for treating COVID-19. For Experiment II, we split the data on virus species, meaning the models were forced to predict drugs for a species that it was not trained on, and have to detect peptide substructures in the amino-acid sequences to suggest drugs. In post-processing, we applied a threshold to the sigmoid function outputs of the neural network, where we assigned each drug a probability of being a potential antiviral for a given amino acid sequence.	./cache/cord-203232-1nnqx1g9.txt	./txt/cord-203232-1nnqx1g9.txt