Literature DB >> 35672769

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

Barbara R Terlouw1, Sophie P J M Vromans2, Marnix H Medema3.   

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 .
© 2022. The Author(s).

Entities:  

Keywords:  Cheminformatics kit; In silico chemistry; Molecular fingerprinting; Python; Structure visualisation

Year:  2022        PMID: 35672769      PMCID: PMC9172152          DOI: 10.1186/s13321-022-00616-5

Source DB:  PubMed          Journal:  J Cheminform        ISSN: 1758-2946            Impact factor:   8.489


  13 in total

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Journal:  Nucleic Acids Res       Date:  2021-07-02       Impact factor: 16.971

3.  Large-scale ligand-based predictive modelling using support vector machines.

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Journal:  J Cheminform       Date:  2016-08-10       Impact factor: 5.514

4.  ChEBI in 2016: Improved services and an expanding collection of metabolites.

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Journal:  Nucleic Acids Res       Date:  2015-10-13       Impact factor: 16.971

5.  The Natural Products Atlas: An Open Access Knowledge Base for Microbial Natural Products Discovery.

Authors:  Jeffrey A van Santen; Grégoire Jacob; Amrit Leen Singh; Victor Aniebok; Marcy J Balunas; Derek Bunsko; Fausto Carnevale Neto; Laia Castaño-Espriu; Chen Chang; Trevor N Clark; Jessica L Cleary Little; David A Delgadillo; Pieter C Dorrestein; Katherine R Duncan; Joseph M Egan; Melissa M Galey; F P Jake Haeckl; Alex Hua; Alison H Hughes; Dasha Iskakova; Aswad Khadilkar; Jung-Ho Lee; Sanghoon Lee; Nicole LeGrow; Dennis Y Liu; Jocelyn M Macho; Catherine S McCaughey; Marnix H Medema; Ram P Neupane; Timothy J O'Donnell; Jasmine S Paula; Laura M Sanchez; Anam F Shaikh; Sylvia Soldatou; Barbara R Terlouw; Tuan Anh Tran; Mercia Valentine; Justin J J van der Hooft; Duy A Vo; Mingxun Wang; Darryl Wilson; Katherine E Zink; Roger G Linington
Journal:  ACS Cent Sci       Date:  2019-11-14       Impact factor: 14.553

6.  COCONUT online: Collection of Open Natural Products database.

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Journal:  J Cheminform       Date:  2021-01-10       Impact factor: 5.514

7.  KNIME-CDK: Workflow-driven cheminformatics.

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Journal:  BMC Bioinformatics       Date:  2013-08-22       Impact factor: 3.169

8.  Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences.

Authors:  Michael A Skinnider; Chad W Johnston; Mathusan Gunabalasingam; Nishanth J Merwin; Agata M Kieliszek; Robyn J MacLellan; Haoxin Li; Michael R M Ranieri; Andrew L H Webster; My P T Cao; Annabelle Pfeifle; Norman Spencer; Q Huy To; Dan Peter Wallace; Chris A Dejong; Nathan A Magarvey
Journal:  Nat Commun       Date:  2020-11-27       Impact factor: 14.919

9.  PubChem in 2021: new data content and improved web interfaces.

Authors:  Sunghwan Kim; Jie Chen; Tiejun Cheng; Asta Gindulyte; Jia He; Siqian He; Qingliang Li; Benjamin A Shoemaker; Paul A Thiessen; Bo Yu; Leonid Zaslavsky; Jian Zhang; Evan E Bolton
Journal:  Nucleic Acids Res       Date:  2021-01-08       Impact factor: 16.971

10.  A Deep Learning Approach to Antibiotic Discovery.

Authors:  Jonathan M Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M Donghia; Craig R MacNair; Shawn French; Lindsey A Carfrae; Zohar Bloom-Ackermann; Victoria M Tran; Anush Chiappino-Pepe; Ahmed H Badran; Ian W Andrews; Emma J Chory; George M Church; Eric D Brown; Tommi S Jaakkola; Regina Barzilay; James J Collins
Journal:  Cell       Date:  2020-02-20       Impact factor: 41.582

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