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UID:submissions.supercomputing.org_SC21_sess181_pap367@linklings.com
SUMMARY:High-Throughput Virtual Screening of Small Molecule Inhibitors for
  SARS-CoV-2 Protein Targets with Deep Fusion Models
DESCRIPTION:Paper\n\nHigh-Throughput Virtual Screening of Small Molecule I
 nhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models\n\nSteven
 son, Jones, Kim, Bennett, Bennion...\n\nStructure-based Deep Fusion models
  were recently shown to outperform several physics- and machine learning-b
 ased protein-ligand binding affinity prediction methods. As part of a mult
 i-institutional COVID-19 pandemic response, over 500 million small molecul
 es were computationally screened against four protein structures from the 
 novel coronavirus (SARS-CoV-2), which causes COVID-19. Three enhancements 
 to Deep Fusion were made in order to evaluate more than five billion docke
 d poses on SARS-CoV-2 protein targets. First, the Deep Fusion concept was 
 refined by formulating the architecture as one, coherently back-propagated
  model (Coherent Fusion) to improve binding-affinity prediction accuracy. 
 Secondly, the model was trained using a distributed, genetic hyper-paramet
 er optimization. Finally, a scalable, high-throughput screening capability
  was developed to maximize the number of ligands evaluated and expedite th
 e path to experimental evaluation. In this work, we present both the metho
 ds developed for machine learning-based high-throughput screening and resu
 lts from using our computational pipeline to find SARS-CoV-2 inhibitors.\n
 \nTag: Algorithms, Applications, Machine Learning and Artificial Intellige
 nce, Numerical Algorithms\n\nRegistration Category: Tech Program Reg Pass
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