Ligand enrichment assessment based on benchmarking data sets has become a necessity for the rational selection of the best-suited approach for prospective data mining of drug-like molecules. Up to now, a variety of benchmarking data sets had been generated and frequently used. Among them, MUBD-HDACs from our prior research efforts was regarded as one of five state-of-the-art benchmarks in 2017 by Frontiers in Pharmacology. This benchmarking set was generated by one of our unique de-biasing algorithms. It also rendered quite a few other cases of successful applications in recent years, thus is expected to have more impact in modern drug discovery. To make our algorithm amenable to more users, we developed a Python GUI application called MUBD-DecoyMaker 2.0. Moreover, it has two new additional functional modules, i. e. "Detect 2D Bias" and "Quality Control". This new GUI version had been proved to be easy to use while generate benchmarking data sets of the same quality. MUBD-DecoyMaker 2.0 is freely available at https://github.com/jwxia2014/MUBD-DecoyMaker2.0, along with its manual and testcase.
Keywords: Python; drug discovery; ligand enrichment; unbiased benchmark; virtual screening.
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