• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

皮卡丘:一款基于Python的用于分析化学单元的信息学工具包。

PIKAChU: a Python-based informatics kit for analysing chemical units.

作者信息

Terlouw Barbara R, Vromans Sophie P J M, Medema Marnix H

机构信息

Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.

出版信息

J Cheminform. 2022 Jun 7;14(1):34. doi: 10.1186/s13321-022-00616-5.

DOI:10.1186/s13321-022-00616-5
PMID:35672769
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9172152/
Abstract

As efforts to computationally describe and simulate the biochemical world become more commonplace, computer programs that are capable of in silico chemistry play an increasingly important role in biochemical research. While such programs exist, they are often dependency-heavy, difficult to navigate, or not written in Python, the programming language of choice for bioinformaticians. Here, we introduce PIKAChU (Python-based Informatics Kit for Analysing CHemical Units): a cheminformatics toolbox with few dependencies implemented in Python. PIKAChU builds comprehensive molecular graphs from SMILES strings, which allow for easy downstream analysis and visualisation of molecules. While the molecular graphs PIKAChU generates are extensive, storing and inferring information on aromaticity, chirality, charge, hybridisation and electron orbitals, PIKAChU limits itself to applications that will be sufficient for most casual users and downstream Python-based tools and databases, such as Morgan fingerprinting, similarity scoring, substructure matching and customisable visualisation. In addition, it comes with a set of functions that assists in the easy implementation of reaction mechanisms. Its minimalistic design makes PIKAChU straightforward to use and install, in stark contrast to many existing toolkits, which are more difficult to navigate and come with a plethora of dependencies that may cause compatibility issues with downstream tools. As such, PIKAChU provides an alternative for researchers for whom basic cheminformatic processing suffices, and can be easily integrated into downstream bioinformatics and cheminformatics tools. PIKAChU is available at https://github.com/BTheDragonMaster/pikachu .

摘要

随着通过计算来描述和模拟生化世界的努力变得越来越普遍,能够进行计算机模拟化学的程序在生化研究中发挥着越来越重要的作用。虽然这类程序已经存在,但它们往往依赖性强、难以操作,或者不是用生物信息学领域首选的编程语言Python编写的。在此,我们介绍PIKAChU(基于Python的化学单元分析信息学工具包):一个用Python实现的、依赖性较少的化学信息学工具箱。PIKAChU能从SMILES字符串构建全面的分子图,便于对分子进行下游分析和可视化。虽然PIKAChU生成的分子图包含丰富信息,可存储和推断关于芳香性、手性、电荷、杂化和电子轨道的信息,但PIKAChU将自身限制在对大多数普通用户以及基于Python的下游工具和数据库足够的应用上,比如摩根指纹识别、相似性评分、子结构匹配和可定制可视化。此外,它还附带了一组有助于轻松实现反应机制的函数。其简约的设计使PIKAChU易于使用和安装,这与许多现有的工具包形成鲜明对比,那些工具包更难操作,且有大量可能导致与下游工具出现兼容性问题的依赖性。因此,PIKAChU为那些只需要基本化学信息学处理的研究人员提供了一个替代方案,并且可以很容易地集成到下游的生物信息学和化学信息学工具中。PIKAChU可在https://github.com/BTheDragonMaster/pikachu获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/150dfbc70b23/13321_2022_616_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/356e1d374055/13321_2022_616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/e6ace68e8c32/13321_2022_616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/710641638262/13321_2022_616_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/150dfbc70b23/13321_2022_616_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/356e1d374055/13321_2022_616_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/e6ace68e8c32/13321_2022_616_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/710641638262/13321_2022_616_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/08a0/9172152/150dfbc70b23/13321_2022_616_Fig7_HTML.jpg

相似文献

1
PIKAChU: a Python-based informatics kit for analysing chemical units.皮卡丘:一款基于Python的用于分析化学单元的信息学工具包。
J Cheminform. 2022 Jun 7;14(1):34. doi: 10.1186/s13321-022-00616-5.
2
plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style.plotnineSeqSuite:一个使用 ggplot2 风格可视化序列数据的 Python 包。
BMC Genomics. 2023 Oct 3;24(1):585. doi: 10.1186/s12864-023-09677-8.
3
Cheminformatics Microservice: unifying access to open cheminformatics toolkits.化学信息学微服务:统一对开放化学信息学工具包的访问。
J Cheminform. 2023 Oct 16;15(1):98. doi: 10.1186/s13321-023-00762-4.
4
NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines.NeuroPycon:一个开源的 Python 工具包,用于快速进行多模态和可重复的脑连接管道。
Neuroimage. 2020 Oct 1;219:117020. doi: 10.1016/j.neuroimage.2020.117020. Epub 2020 Jun 6.
5
Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit.Pybel:用于OpenBabel化学信息学工具包的Python包装器。
Chem Cent J. 2008 Mar 9;2:5. doi: 10.1186/1752-153X-2-5.
6
GUIDEMOL: A Python graphical user interface for molecular descriptors based on RDKit.GUIDEMOL:一个基于 RDKit 的分子描述符的 Python 图形用户界面。
Mol Inform. 2024 Jan;43(1):e202300190. doi: 10.1002/minf.202300190. Epub 2023 Nov 20.
7
RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries.RDCanon:一个用于标准化 SMARTS 查询中令牌顺序的 Python 包。
J Chem Inf Model. 2024 Apr 22;64(8):2948-2954. doi: 10.1021/acs.jcim.4c00138. Epub 2024 Mar 15.
8
CGRtools: Python Library for Molecule, Reaction, and Condensed Graph of Reaction Processing.CGRtools:用于分子、反应和反应处理凝聚图的 Python 库。
J Chem Inf Model. 2019 Jun 24;59(6):2516-2521. doi: 10.1021/acs.jcim.9b00102. Epub 2019 May 28.
9
GenGraph: a python module for the simple generation and manipulation of genome graphs.GenGraph:一个用于简单生成和操作基因组图的 Python 模块。
BMC Bioinformatics. 2019 Oct 25;20(1):519. doi: 10.1186/s12859-019-3115-8.
10
ORBKIT: A modular python toolbox for cross-platform postprocessing of quantum chemical wavefunction data.ORBKIT:一个用于跨平台量子化学波函数数据后处理的模块化 Python 工具包。
J Comput Chem. 2016 Jun 15;37(16):1511-20. doi: 10.1002/jcc.24358. Epub 2016 Apr 4.

引用本文的文献

1
Machine learning: Python tools for studying biomolecules and drug design.机器学习:用于研究生物分子和药物设计的Python工具。
Mol Divers. 2025 Apr 29. doi: 10.1007/s11030-025-11199-2.
2
RAIChU: automating the visualisation of natural product biosynthesis.RAIChU:实现天然产物生物合成可视化的自动化
J Cheminform. 2024 Sep 3;16(1):106. doi: 10.1186/s13321-024-00898-x.
3
Python tools for structural tasks in chemistry.用于化学结构任务的Python工具。

本文引用的文献

1
antiSMASH 6.0: improving cluster detection and comparison capabilities.antiSMASH 6.0:提高簇检测和比较能力。
Nucleic Acids Res. 2021 Jul 2;49(W1):W29-W35. doi: 10.1093/nar/gkab335.
2
COCONUT online: Collection of Open Natural Products database.COCONUT在线:开放天然产物数据库集合。
J Cheminform. 2021 Jan 10;13(1):2. doi: 10.1186/s13321-020-00478-9.
3
Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.从微生物基因组序列中综合预测次生代谢物结构和生物活性。
Mol Divers. 2024 May 14. doi: 10.1007/s11030-024-10889-7.
4
Optimisation of surfactin yield in using data-efficient active learning and high-throughput mass spectrometry.利用数据高效主动学习和高通量质谱法优化表面活性素产量。
Comput Struct Biotechnol J. 2024 Feb 15;23:1226-1233. doi: 10.1016/j.csbj.2024.02.012. eCollection 2024 Dec.
5
Cheminformatics Microservice: unifying access to open cheminformatics toolkits.化学信息学微服务:统一对开放化学信息学工具包的访问。
J Cheminform. 2023 Oct 16;15(1):98. doi: 10.1186/s13321-023-00762-4.
6
DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications.DECIMER.ai:一个用于科学出版物中光学化学结构自动识别、分割和识别的开放平台。
Nat Commun. 2023 Aug 19;14(1):5045. doi: 10.1038/s41467-023-40782-0.
7
MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters.MIBiG 3.0:一个社区驱动的努力,用于注释经过实验验证的生物合成基因簇。
Nucleic Acids Res. 2023 Jan 6;51(D1):D603-D610. doi: 10.1093/nar/gkac1049.
Nat Commun. 2020 Nov 27;11(1):6058. doi: 10.1038/s41467-020-19986-1.
4
PubChem in 2021: new data content and improved web interfaces.PubChem 在 2021 年:新增数据内容和改进的网络界面。
Nucleic Acids Res. 2021 Jan 8;49(D1):D1388-D1395. doi: 10.1093/nar/gkaa971.
5
A Deep Learning Approach to Antibiotic Discovery.深度学习在抗生素发现中的应用。
Cell. 2020 Feb 20;180(4):688-702.e13. doi: 10.1016/j.cell.2020.01.021.
6
The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.《天然产物图谱:微生物天然产物发现的开放获取知识库》
ACS Cent Sci. 2019 Nov 27;5(11):1824-1833. doi: 10.1021/acscentsci.9b00806. Epub 2019 Nov 14.
7
SmilesDrawer: Parsing and Drawing SMILES-Encoded Molecular Structures Using Client-Side JavaScript.SmilesDrawer:使用客户端 JavaScript 解析和绘制 SMILES 编码的分子结构。
J Chem Inf Model. 2018 Jan 22;58(1):1-7. doi: 10.1021/acs.jcim.7b00425. Epub 2018 Jan 11.
8
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.化学开发工具包(CDK)v2.0:原子类型标注、描绘、分子式及子结构搜索。
J Cheminform. 2017 Jun 6;9(1):33. doi: 10.1186/s13321-017-0220-4.
9
Large-scale ligand-based predictive modelling using support vector machines.使用支持向量机的基于配体的大规模预测建模。
J Cheminform. 2016 Aug 10;8:39. doi: 10.1186/s13321-016-0151-5. eCollection 2016.
10
ChEBI in 2016: Improved services and an expanding collection of metabolites.2016年的ChEBI:服务改进与代谢物集合的扩充
Nucleic Acids Res. 2016 Jan 4;44(D1):D1214-9. doi: 10.1093/nar/gkv1031. Epub 2015 Oct 13.