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DTSTAMP:20211207T054724Z
LOCATION:240-241-242
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UID:submissions.supercomputing.org_SC21_sess249_gbv105@linklings.com
SUMMARY:FEP-Based Large-Scale Virtual Screening for Effective Drug Discove
 ry against COVID-19
DESCRIPTION:ACM Gordon Bell Finalist, Awards Presentation\n\nFEP-Based Lar
 ge-Scale Virtual Screening for Effective Drug Discovery against COVID-19\n
 \nLi, Wu, Li, Liu, Lu...\n\nAs a theoretically rigorous and accurate metho
 d, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calcul
 ations showed great potential in drug discovery, but its practical applica
 tion was difficult due to high computational cost. To rapidly discover ant
 iviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-
 based virtual screening for ∼12,000 protein-ligand binding systems o
 n a new generation of Tianhe supercomputer. A task management tool was spe
 cifically developed for automating the whole process involving more than 0
 .5 million MD tasks. In further experimental validation, 50 out of 98 test
 ed compounds showed significant inhibitory activity towards Mpro, and one 
 representative inhibitor, dipyridamole, showed remarkable outcomes in subs
 equent clinical trials. This work not only demonstrates the potential of F
 EP-ABFE in drug discovery, but also provides an excellent starting point f
 or further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of
  data generated in this work will also accelerate the further development 
 of FEP-related methods.\n\nTag: AI-HPC Convergence, Computational Science,
  Extreme Scale Computing, Performance, Scientific Computing\n\nRegistratio
 n Category: Tech Program Reg Pass
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