Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.
A AcharyaR AgarwalM BakerJ BaudryD BhowmikS BoehmK G BylerL CoatesS Y ChenC J CooperO DemerdashI DaidoneJ D EblenS EllingsonS ForliJ GlaserJ C GumbartJ GunnelsO HernandezS IrleJ LarkinT J LawrenceS LeGrandS-H LiuJ C MitchellG ParkJ M ParksA PavlovaL PetridisD PooleL PouchardA RamanathanD RogersD Santos-MartinsA ScheinbergA SedovaS ShenJ C SmithM D SmithC SotoA TsarisM ThavappiragasamA F TillackJ V VermaasV Q VuongJ YinS YooM ZahranL Zanetti-PolziPublished in: ChemRxiv : the preprint server for chemistry (2020)
We present a supercomputer-driven pipeline for <i>in-silico</i> drug 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 AI methods to cluster MD trajectories and rescore docking poses.
Keyphrases
- molecular dynamics
- drug discovery
- sars cov
- density functional theory
- machine learning
- respiratory syndrome coronavirus
- convolutional neural network
- artificial intelligence
- coronavirus disease
- mass spectrometry
- depressive symptoms
- neural network
- big data
- protein protein
- ms ms
- molecular dynamics simulations
- binding protein
- quantum dots