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Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.
Venkatraman, Vishwesh; Colligan, Thomas H; Lesica, George T; Olson, Daniel R; Gaiser, Jeremiah; Copeland, Conner J; Wheeler, Travis J; Roy, Amitava.
  • Venkatraman V; Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway.
  • Colligan TH; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Lesica GT; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Olson DR; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Gaiser J; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Copeland CJ; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Wheeler TJ; Department of Computer Science, University of Montana, Missoula, MT, United States.
  • Roy A; Department of Computer Science, University of Montana, Missoula, MT, United States.
Front Pharmacol ; 13: 874746, 2022.
Article in English | MEDLINE | ID: covidwho-1952525
ABSTRACT
The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods for identification of therapeutic drugs, and in particular has laid bare the need for methods that allow exploration of the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods hold the promise of enabling appraisal of billions of candidate molecules, thus expanding the search space while concurrently reducing costs and speeding discovery. Here, we describe a new screening pipeline, called drugsniffer, that is capable of rapidly exploring drug candidates from a library of billions of molecules, and is designed to support distributed computation on cluster and cloud resources. As an example of performance, our pipeline required ∼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of ∼3.7 billion candidate molecules.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Front Pharmacol Year: 2022 Document Type: Article Affiliation country: Fphar.2022.874746

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Front Pharmacol Year: 2022 Document Type: Article Affiliation country: Fphar.2022.874746