Scopy

Scopy screens chemical libraries to identify and filter out undesirable compounds that compromise enrichment in high-throughput screening (HTS) and virtual screening (VS).


Key Features:

  • Data Preparation: Modules for preparing chemical datasets prior to screening.
  • Descriptor Calculation: Calculates 39 basic molecular descriptors for evaluating compound properties.
  • Scaffold and Substructure Analysis: Computes two types of molecular scaffolds and six substructure descriptors for structural analysis.
  • Fingerprinting: Computes two types of chemical fingerprints.
  • Drug-likeness Rules: Implements 15 drug-likeness rules, comprising 13 specific rules and 2 building-block rules.
  • Frequent Hitter Rules: Implements eight rules to identify frequent hitters, including four assay-interference substructure filters and four promiscuous compound substructure filters.
  • Toxicophore Filters: Includes 11 toxicophore filters: five human-related, three environment-related, and three comprehensive toxicity substructure filters.
  • Visualization Tools: Generates four visualization outputs: basic feature radar chart, feature-feature scatter diagram, functional group marker gram, and cloud gram.

Scientific Applications:

  • Chemical Library Quality Control: Filters out compounds with undesirable properties or substructures to improve the quality of screening libraries for HTS and VS.
  • Early Drug Discovery Screening: Reduces noisy compounds that affect drug-likeness, selectivity, or toxicity assessments during early-stage drug discovery.
  • Library Design and Candidate Prioritization: Supports the design of higher-quality chemical libraries and increases reliability of identifying potential therapeutic candidates in HTS and VS campaigns.

Methodology:

Computational steps explicitly include chemical data preparation; calculation of 39 molecular descriptors; computation of two scaffold types and six substructure descriptors; calculation of two fingerprint types; application of 15 drug-likeness rules (13 specific, 2 building-block), eight frequent-hitter rules (4 assay-interference, 4 promiscuous), and 11 toxicophore filters (5 human-related, 3 environment-related, 3 comprehensive); and generation of four visualization outputs (basic feature radar chart, feature-feature scatter diagram, functional group marker gram, cloud gram).

Topics

Details

License:
MIT
Programming Languages:
Python, Java
Added:
1/18/2021
Last Updated:
2/13/2021

Operations

Publications

Yang Z, Yang Z, Lu A, Hou T, Cao D. Scopy: an integrated negative design python library for desirable HTS/VS database design. Briefings in Bioinformatics. 2020;22(3). doi:10.1093/bib/bbaa194. PMID:32892221.

PMID: 32892221
Funding: - Key Research and Development Program of Zhejiang Province: 2019C03G2010942 - National Natural Science Foundation of China: 21575128, 81773632 - Zhejiang Provincial Natural Science Foundation: LZ19H300001 - HKBU Strategic Development Fund: SDF19–0402–P02

Links