id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-222664-4qyrtzhu	Coban, Mathew	Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement	2020-07-06		.txt	text/plain	11220	638	46	We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors; S/Ace2, Tmprss2, Cathepsins L and K, and Mpro to prevent binding, membrane fusion and replication of the virus, respectively. Using a computational pipeline that aimed to expeditiously identify lead compounds against COVID-19, we combined compound library preparation, molecular modeling, and structure simulations to generate an ensemble of conformations and increase high quality docking outcomes against two essential SARS-CoV-2 viral proteins and their host protein interactions; S/Ace2, Tmprss2, Cathepsin L and K, and M pro that are known to control both viral binding, entry and virus replication (Fig. 1A) .	./cache/cord-222664-4qyrtzhu.txt	./txt/cord-222664-4qyrtzhu.txt