Scopy 다운로드 Scopy 소스 코드 다운로드

Scopy

AI 소스 코드

fixed mistake in ro5

다운로드

SCOPY : 바람직한 HTS/VS 데이터베이스 디자인을위한 통합 네거티브 디자인 파이썬 라이브러리

개요

초기 약물 발견에서 바람직하지 않은 화합물 을 스크리닝하도록 설계된 통합 된 네거티브 디자인 파이썬 라이브러리 인 Scopy ( S Crenning Copounds). SCOPY에는 6 개의 모듈, 데이터 준비 커버 , 스크리닝 필터 , 스캐 폴드 및 디스크립터 계산 및 시각화 분석이 포함됩니다.

설치

RDKIT를 설치하십시오

 >>> conda install -c conda-forge rdkit

Scopy를 설치하십시오

Scopy는 Python3 Enviroment의 Linux, OSX 및 Windows 시스템에서 성공적으로 테스트되었습니다.

원천

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

콘다

 >>> conda install -c kotori_y scopy

pypi

 >>> pip install scopy

선적 서류 비치

(1) 문서의 온라인 버전은 여기에서 확인할 수 있습니다 : https://scopy.iamkotori.com/
(2) 빠른 시작 예 : https://scopy.iamkotori.com/user_guide.html
(3) 응용 프로그램 예제 (파이프 라인) : https://scopy.iamkotori.com/application.html

연락하다

질문이나 제안이 있으면 다음과 같이 문의하십시오 : [email protected] 및 [email protected].
이 소프트웨어 및 관련 데이터 및 문서를 다루는 "MIT"라이센스에 대한 자세한 내용은 파일 라이센스를 참조하십시오.

우리를 인용하십시오

Yang ZY, Yang ZJ, Lu AP, Hou TJ, Cao DS. SCOPY : 바람직한 HTS/vs 데이터베이스 설계를위한 통합 네거티브 디자인 파이썬 라이브러리 [2020 년 9 월 7 일 인쇄에 앞서 온라인으로 게시]. 간단한 생물 정보 . 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},
}