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Scopy

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Scopy：綜合負面設計Python庫，用於理想的HTS/VS數據庫設計

概述

Scopy （ py thon中的S Crenning CoMpounds ），這是一個綜合的負設計Python庫，旨在篩選出早期藥物發現中不良化合物。 SCOPY包括六個模塊，涵蓋數據準備，篩選過濾器，腳手架和描述符的計算以及可視化分析。

安裝

安裝rdkit

 >>> conda install -c conda-forge rdkit

安裝Scopy

Scopy已在Python3 EnviroMent下成功地在Linux，OSX和Windows系統上進行了測試。

來源

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

康達

 >>> conda install -c kotori_y scopy

PYPI

 >>> pip install scopy

文件

（1）文檔的在線版本可在此處找到：https：//scopy.iamkotori.com/
（2）快速啟動示例：https：//scopy.iamkotori.com/user_guide.html
（3）申請示例（管道）：https：//scopy.iamkotori.com/application.html

接觸

如果您有疑問或建議，請聯繫：[email protected]和[email protected]。
有關“ MIT”許可證的詳細信息，請參閱文件許可證，該許可涵蓋了該軟件及其相關的數據和文檔。

引用我們

Yang Zy，Yang ZJ，Lu AP，Hou TJ，Cao ds。 Scopy：用於理想的HTS/vs數據庫設計的集成負面設計Python庫[在在線發布，在線發布，2020年9月7日]。簡短的生物知識。 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},
}