The Scopy’s documentation

High-throughput screening (HTS) and virtual screening (VS) are widely applied in compounds screening and lead discovery. However, the frequent appearances of “noisy compounds” in the screening database, such as compounds with poor drug-likeness, compounds with poor selectivity or compounds with potential toxicity, have greatly weakened the efficiency of HTS and VS campaigns. The success of screening results critically depends on the quality of the available screening libraries. To construct a high-quality database, we developed Scopy (Screnning COmpounds in PYthon), an integrated negative design python library designed for screening out undesiable compounds in the early drug discovery. Scopy includes six modules, covering data preparation, screening filters, the calculation of scaffolds and descriptors, and the visualization analysis.

The Python package Scopy is designed by CBDD Group (Computational Biology & Drug Design Group), Xiangya School of Pharmaceutical Sciences, Central South University.


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