id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-344934-m0q7rm6z	Mahapatra, Sovesh	Repurposing Therapeutics for COVID-19: Rapid Prediction of Commercially available drugs through Machine Learning and Docking	2020-04-07		.txt	text/plain	3876	228	56	Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Bioactivity datasets which are available from the numerous high throughput screens deliver useful means for machine learning classifiers as they contain binary information (active/inactive) as well as numerical values to classify different compounds under consideration 22, 23 . These drugs were downloaded in the form of SDFs and after processing, the descriptions generated were taken as the test model for developing the train model which was made on the basis of a database containing the inhibitors of the SARS coronavirus. Around 178 drugs were predicted by our ML model which can be effective for the treatment of diseases caused by SARS-Cov-2.	./cache/cord-344934-m0q7rm6z.txt	./txt/cord-344934-m0q7rm6z.txt