Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets

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 screen...

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Main Authors: Vishwesh Venkatraman (Author), Thomas H. Colligan (Author), George T. Lesica (Author), Daniel R. Olson (Author), Jeremiah Gaiser (Author), Conner J. Copeland (Author), Travis J. Wheeler (Author), Amitava Roy (Author)
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Published: Frontiers Media S.A., 2022-04-01T00:00:00Z.
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100 1 0 |a Vishwesh Venkatraman  |e author 
700 1 0 |a Thomas H. Colligan  |e author 
700 1 0 |a George T. Lesica  |e author 
700 1 0 |a Daniel R. Olson  |e author 
700 1 0 |a Jeremiah Gaiser  |e author 
700 1 0 |a Conner J. Copeland  |e author 
700 1 0 |a Travis J. Wheeler  |e author 
700 1 0 |a Amitava Roy  |e author 
700 1 0 |a Amitava Roy  |e author 
245 0 0 |a Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets 
260 |b Frontiers Media S.A.,   |c 2022-04-01T00:00:00Z. 
500 |a 1663-9812 
500 |a 10.3389/fphar.2022.874746 
520 |a 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. 
546 |a EN 
690 |a virtual screeening 
690 |a machine learning 
690 |a computer aided drug design 
690 |a de novo design 
690 |a SARS-C0V-2 
690 |a protein-ligand docking 
690 |a Therapeutics. Pharmacology 
690 |a RM1-950 
655 7 |a article  |2 local 
786 0 |n Frontiers in Pharmacology, Vol 13 (2022) 
787 0 |n https://www.frontiersin.org/articles/10.3389/fphar.2022.874746/full 
787 0 |n https://doaj.org/toc/1663-9812 
856 4 1 |u https://doaj.org/article/680ce8edbd9340dbb4f37fb3694e2e94  |z Connect to this object online.