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Scopy

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Scopy：综合负面设计Python库，用于理想的HTS/VS数据库设计

概述

Scopy （ py thon中的S Crenning CoMpounds ），这是一个综合的负设计Python库，旨在筛选出早期药物发现中不良化合物。 SCOPY包括六个模块，涵盖数据准备，筛选过滤器，脚手架和描述符的计算以及可视化分析。

安装

安装rdkit

 >>> conda install -c conda-forge rdkit

安装Scopy

Scopy已在Python3 EnviroMent下成功地在Linux，OSX和Windows系统上进行了测试。

来源

 >>> git clone [email protected]:kotori-y/Scopy.git && cd scopy
>>> [sudo] python setup.py install

康达

 >>> conda install -c kotori_y scopy

PYPI

 >>> pip install scopy

文档

（1）文档的在线版本可在此处找到：https：//scopy.iamkotori.com/
（2）快速启动示例：https：//scopy.iamkotori.com/user_guide.html
（3）申请示例（管道）：https：//scopy.iamkotori.com/application.html

接触

如果您有疑问或建议，请联系：[email protected]和[email protected]。
有关“ MIT”许可证的详细信息，请参阅文件许可证，该许可涵盖了该软件及其相关的数据和文档。

引用我们

Yang Zy，Yang ZJ，Lu AP，Hou TJ，Cao ds。 Scopy：用于理想的HTS/vs数据库设计的集成负面设计Python库[在在线发布，在线发布，2020年9月7日]。简短的生物知识。 2020; BBAA194。 doi：10.1093/bib/bbaa194

 @article{10.1093/bib/bbaa194,
    author = {Yang, Zi-Yi and Yang, Zhi-Jiang and Lu, Ai-Ping and Hou, Ting-Jun and Cao, Dong-Sheng},
    title = "{Scopy: an integrated negative design python library for desirable HTS/VS database design}",
    journal = {Briefings in Bioinformatics},
    year = {2020},
    month = {09},
    abstract = "{High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of ‘noisy compounds’ in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery.In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram.Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.}",
    issn = {1477-4054},
    doi = {10.1093/bib/bbaa194},
    url = {https://doi.org/10.1093/bib/bbaa194},
    note = {bbaa194},
    eprint = {https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbaa194/33719387/bbaa194.pdf},
}