Drugsniffer: An open source workflow for virtually screening billions of molecules for binding affinity to protein targets

Authors

• KS Hsu
• PB Olkhanud
• IK Luvaga
• E Kanyo
• SK Richardson
• N Sassu
• A Howell
• JA Berzofsky

Abstract

The SARS-CoV-2 pandemic has highlighted the importance of efficient and effective therapeutic drug identification. In particular, the pandemic has laid bare the need for ways to explore the full diversity of synthesizable small molecules. While classical high-throughput screening methods may consider up to millions of molecules, virtual screening methods promise to appraise 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, which can rapidly explore 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.

Scientific Focus Area: Computational Biology

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