xv6m46c2

Property	Value
?:abstract	We present a supercomputer-driven pipeline for iin-silicodrug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2 THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and Amethods to cluster MD trajectories and rescore docking poses 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> <https://research.tib.eu/covid-19/entity/Atanu%2C_Acharya_Rupesh_Agarwal_Matthew_Baker_Jerome_Baudry_Debsindhu_Bhowmik_Swen_Boehm_Kendall_Byler_Leighton_Coates_Sam_Yen-Chi_Chen_Connor_J_Cooper_Omar_Demerdash_Isabella_Daidone_John_Eblen_Sally_R._Ellingson_Stefano_Forli_Jens_Glaser_James_C_Gumbart_John_Gunnels_Oscar_Hernandez_Stephan_Irle_Jeffery_Larkin_Travis_J_Lawrence_Scott_LeGrand_Shih-Hsien_Liu_Julie_C._Mitchell_Gilchan_Park_Jerry_M_Parks_Anna_Pavlova_Loukas_Petridis_Duncan_Poole_Line_Pouchard_Arvind_Ramanathan_David_Rogers_Diogo_Santos-Martins_Aaron_Scheinberg_Ada_Sedova_Shawn_Shen_Jeremy_C_Smith_Micholas_Smith_Carlos_Soto_Aristides_Tsaris_Mathialakan_Thavappiragasam_Andreas_F_Tillack_Josh_V_Vermaas_Van_Quan_Vuong_Junqi_Yin_Shinjae_Yoo_Mai_Zahran_Laura_Zanetti-Polzi>
?:journal	J_Chem_Inf_Model
?:license	unk
?:publication_isRelatedTo_Disease	<https://research.tib.eu/covid-19/entity/COVID-19>
?:source	WHO
?:title	Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19
?:type	<https://research.tib.eu/covid-19/vocab/Publication>
?:who_covidence_id	#1065780 #38 #978490
?:year	2020

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Value

?:abstract

We present a supercomputer-driven pipeline for iin-silicodrug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2 THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and Amethods to cluster MD trajectories and rescore docking poses
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