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

Benchmarking is a
crucial step in evaluating virtual screening
methods for drug discovery. One major issue that arises among benchmarking
data sets 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 create
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 the 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 data sets
and access the corresponding information directly from the software
package. Currently, it only takes DUBS less than 2 min to create a
benchmark using this format. Since DUBS uses a simple python script,
users can easily modify this to create more complex benchmarks. We
hope that DUBS will be a useful community resource to provide a standardized
representation for benchmarking data sets in virtual screening. The
DUBS package is available on GitHub at https://github.com/chopralab/lemon/tree/master/dubs.