Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.
J Chem Inf Model
; 60(12): 5832-5852, 2020 12 28.
Article
Dans Anglais
| MEDLINE | ID: covidwho-1065780
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Texte intégral:
Disponible
Collection:
Bases de données internationales
Base de données:
MEDLINE
Sujet Principal:
Antiviraux
/
Protéines virales non structurales
/
SARS-CoV-2
/
Type d'étude:
Étude pronostique
Les sujets:
Médecine traditionnelle
Limites du sujet:
Humains
langue:
Anglais
Revue:
J Chem Inf Model
Thème du journal:
Informatique médicale
/
Chimie
Année:
2020
Type de document:
Article
Pays d'affiliation:
Acs.jcim.0c01010
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