Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.
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 â¼40,000 total compute hours to screen for potential drugs targeting three SARS-CoV2 proteins among a library of â¼3.7 billion candidate molecules.
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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