DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

Abstract

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking datasets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly created benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking datasets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 minutes to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking datasets in virtual screening.