gcn4

Property	Value
?:abstract	[Image: see text] We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
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?:creator	<https://research.tib.eu/covid-19/entity/Acharya%2C_A.%3B_Agarwal%2C_R.%3B_Baker%2C_M._B.%3B_Baudry%2C_J.%3B_Bhowmik%2C_D.%3B_Boehm%2C_S.%3B_Byler%2C_K._G.%3B_Chen%2C_S._Y.%3B_Coates%2C_L.%3B_Cooper%2C_C._J.%3B_Demerdash%2C_O.%3B_Daidone%2C_I.%3B_Eblen%2C_J._D.%3B_Ellingson%2C_S.%3B_Forli%2C_S.%3B_Glaser%2C_J.%3B_Gumbart%2C_J._C.%3B_Gunnels%2C_J.%3B_Hernandez%2C_O.%3B_Irle%2C_S.%3B_Kneller%2C_D._W.%3B_Kovalevsky%2C_A.%3B_Larkin%2C_J.%3B_Lawrence%2C_T._J.%3B_LeGrand%2C_S.%3B_Liu%2C_S.-H.%3B_Mitchell%2C_J.C.%3B_Park%2C_G.%3B_Parks%2C_J.M.%3B_Pavlova%2C_A.%3B_Petridis%2C_L.%3B_Poole%2C_D.%3B_Pouchard%2C_L.%3B_Ramanathan%2C_A.%3B_Rogers%2C_D._M.%3B_Santos-Martins%2C_D.%3B_Scheinberg%2C_A.%3B_Sedova%2C_A.%3B_Shen%2C_Y.%3B_Smith%2C_J._C.%3B_Smith%2C_M._D.%3B_Soto%2C_C.%3B_Tsaris%2C_A.%3B_Thavappiragasam%2C_M.%3B_Tillack%2C_A._F.%3B_Vermaas%2C_J._V.%3B_Vuong%2C_V._Q.%3B_Yin%2C_J.%3B_Yoo%2C_S.%3B_Zahran%2C_M.%3B_Zanetti-Polzi%2C_L.>
?:doi	10.1021/acs.jcim.0c01010
?:doi	<https://research.tib.eu/covid-19/entity/10.1021/acs.jcim.0c01010>
?:journal	J_Chem_Inf_Model
?:license	no-cc
?:pdf_json_files	document_parses/pdf_json/a0cda5d1bf47b8b2708ec82db11c2e95eca7b354.json
?:pmc_json_files	document_parses/pmc_json/PMC7754786.xml.json
?:pmcid	<https://research.tib.eu/covid-19/entity/PMC7754786>
?:pmid	<https://research.tib.eu/covid-19/entity/33326239.0>
?:pmid	33326239.0
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:sha_id	<https://research.tib.eu/covid-19/entity/a0cda5d1bf47b8b2708ec82db11c2e95eca7b354>
?:source	Medline; PMC
?:title	Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:year	2020-12-16

Property

Value

?:abstract

[Image: see text] We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

is ?:annotates of