Example of Journal of Cheminformatics format
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Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format
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Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format Example of Journal of Cheminformatics format
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open access Open Access ISSN: 17582946
recommended Recommended

Journal of Cheminformatics — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Library and Information Sciences #6 of 235 up up by 3 ranks
Computer Graphics and Computer-Aided Design #7 of 88 up up by 1 rank
Computer Science Applications #52 of 693 up up by 3 ranks
Physical and Theoretical Chemistry #14 of 169 up up by 16 ranks
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 270 Published Papers | 2569 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 30/06/2020
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FAQ

Journal Performance & Insights

  • Impact Factor
  • CiteRatio
  • SJR
  • SNIP

Impact factor determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

5.318

28% from 2018

Impact factor for Journal of Cheminformatics from 2016 - 2019
Year Value
2019 5.318
2018 4.154
2017 3.893
2016 4.22
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 28% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

CiteRatio is a measure of average citations received per peer-reviewed paper published in the journal.

9.5

22% from 2019

CiteRatio for Journal of Cheminformatics from 2016 - 2020
Year Value
2020 9.5
2019 7.8
2018 7.6
2017 6.6
2016 6.2
graph view Graph view
table view Table view

insights Insights

  • CiteRatio of this journal has increased by 22% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR) measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

1.35

6% from 2019

SJR for Journal of Cheminformatics from 2016 - 2020
Year Value
2020 1.35
2019 1.43
2018 1.499
2017 1.203
2016 1.511
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 6% in last years.
  • This journal’s SJR is in the top 10 percentile category.

Source Normalized Impact per Paper (SNIP) measures actual citations received relative to citations expected for the journal's category.

1.744

13% from 2019

SNIP for Journal of Cheminformatics from 2016 - 2020
Year Value
2020 1.744
2019 1.541
2018 1.464
2017 1.289
2016 1.363
graph view Graph view
table view Table view

insights Insights

  • SNIP of this journal has increased by 13% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

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Journal of Cheminformatics

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Springer

Journal of Cheminformatics

Journal of Cheminformatics is an open access journal publishing original peer-reviewed research in all aspects of cheminformatics and molecular modelling. Coverage includes, but is not limited to: chemical information systems, software and databases, and molecular modelling; c...... Read More

Library and Information Sciences

Computer Graphics and Computer-Aided Design

Computer Science Applications

Physical and Theoretical Chemistry

Social Sciences

i
Last updated on
30 Jun 2020
i
ISSN
1758-2946
i
Impact Factor
High - 1.334
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Numbered
[25]
i
Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent con-version. Phys Rev B. 1982;25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1758-2946-3-33
Open Babel: An open chemical toolbox

Abstract:

A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to... A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org . read more read less

Topics:

Cheminformatics (54%)54% related to the paper, Chemical Markup Language (54%)54% related to the paper, File format (51%)51% related to the paper, Canonicalization (51%)51% related to the paper
View PDF
4,156 Citations
open accessOpen access Journal Article DOI: 10.1186/1758-2946-4-17
Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

Abstract:

The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molec... The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net . read more read less

Topics:

Molecule editor (56%)56% related to the paper, Application programming interface (53%)53% related to the paper, Visualization (50%)50% related to the paper
View PDF
3,987 Citations
open accessOpen access Journal Article DOI: 10.1186/1758-2946-6-13
TCMSP: a database of systems pharmacology for drug discovery from herbal medicines.
Jinlong Ru1, Peng Li1, Wang Jinan1, Wei Zhou1, Bohui Li1, Chao Huang1, Pidong Li1, Zihu Guo1, Weiyang Tao1, Yinfeng Yang2, Xue Xu1, Yan Li2, Yonghua Wang1, Ling Yang3

Abstract:

Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platfo... Modern medicine often clashes with traditional medicine such as Chinese herbal medicine because of the little understanding of the underlying mechanisms of action of the herbs. In an effort to promote integration of both sides and to accelerate the drug discovery from herbal medicines, an efficient systems pharmacology platform that represents ideal information convergence of pharmacochemistry, ADME properties, drug-likeness, drug targets, associated diseases and interaction networks, are urgently needed. The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was built based on the framework of systems pharmacology for herbal medicines. It consists of all the 499 Chinese herbs registered in the Chinese pharmacopoeia with 29,384 ingredients, 3,311 targets and 837 associated diseases. Twelve important ADME-related properties like human oral bioavailability, half-life, drug-likeness, Caco-2 permeability, blood-brain barrier and Lipinski’s rule of five are provided for drug screening and evaluation. TCMSP also provides drug targets and diseases of each active compound, which can automatically establish the compound-target and target-disease networks that let users view and analyze the drug action mechanisms. It is designed to fuel the development of herbal medicines and to promote integration of modern medicine and traditional medicine for drug discovery and development. The particular strengths of TCMSP are the composition of the large number of herbal entries, and the ability to identify drug-target networks and drug-disease networks, which will help revealing the mechanisms of action of Chinese herbs, uncovering the nature of TCM theory and developing new herb-oriented drugs. TCMSP is freely available at http://sm.nwsuaf.edu.cn/lsp/tcmsp.php . read more read less

Topics:

Systems pharmacology (61%)61% related to the paper, Modern medicine (54%)54% related to the paper, Drug discovery (51%)51% related to the paper
View PDF
1,150 Citations
open accessOpen access Journal Article DOI: 10.1186/S13321-017-0235-X
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona1, Thomas Blaschke1, Ola Engkvist1, Hongming Chen1

Abstract:

This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query struct... This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model. read more read less

Topics:

Generative model (55%)55% related to the paper
View PDF
494 Citations
open accessOpen access Journal Article DOI: 10.1186/S13321-015-0069-3
Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Dávid Bajusz1, Anita Rácz1, Anita Rácz2, Károly Héberger1

Abstract:

Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/dis... Cheminformaticians are equipped with a very rich toolbox when carrying out molecular similarity calculations. A large number of molecular representations exist, and there are several methods (similarity and distance metrics) to quantify the similarity of molecular representations. In this work, eight well-known similarity/distance metrics are compared on a large dataset of molecular fingerprints with sum of ranking differences (SRD) and ANOVA analysis. The effects of molecular size, selection methods and data pretreatment methods on the outcome of the comparison are also assessed. A supplier database ( https://mcule.com/ ) was used as the source of compounds for the similarity calculations in this study. A large number of datasets, each consisting of one hundred compounds, were compiled, molecular fingerprints were generated and similarity values between a randomly chosen reference compound and the rest were calculated for each dataset. Similarity metrics were compared based on their ranking of the compounds within one experiment (one dataset) using sum of ranking differences (SRD), while the results of the entire set of experiments were summarized on box and whisker plots. Finally, the effects of various factors (data pretreatment, molecule size, selection method) were evaluated with analysis of variance (ANOVA). This study complements previous efforts to examine and rank various metrics for molecular similarity calculations. Here, however, an entirely general approach was taken to neglect any a priori knowledge on the compounds involved, as well as any bias introduced by examining only one or a few specific scenarios. The Tanimoto index, Dice index, Cosine coefficient and Soergel distance were identified to be the best (and in some sense equivalent) metrics for similarity calculations, i.e. these metrics could produce the rankings closest to the composite (average) ranking of the eight metrics. The similarity metrics derived from Euclidean and Manhattan distances are not recommended on their own, although their variability and diversity from other similarity metrics might be advantageous in certain cases (e.g. for data fusion). Conclusions are also drawn regarding the effects of molecule size, selection method and data pretreatment on the ranking behavior of the studied metrics. read more read less

Topics:

Similarity (network science) (64%)64% related to the paper, Ranking (information retrieval) (52%)52% related to the paper
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483 Citations
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Journal of Cheminformatics format uses SPBASIC citation style.

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SciSpace allows imports from all reference managers like Mendeley, Zotero, Endnote, Google Scholar etc.

Frequently asked questions

Absolutely not! With our tool, you can freely write without having to focus on LaTeX. You can write your entire paper as per the Journal of Cheminformatics guidelines and autoformat it.

Yes. The template is fully compliant as per the guidelines of this journal. Our experts at SciSpace ensure that. Also, if there's any update in the journal format guidelines, we take care of it and include that in our algorithm.

Sure. We support all the top citation styles like APA style, MLA style, Vancouver style, Harvard style, Chicago style, etc. For example, in case of this journal, when you write your paper and hit autoformat, it will automatically update your article as per the Journal of Cheminformatics citation style.

You can avail our Free Trial for 7 days. I'm sure you'll find our features very helpful. Plus, it's quite inexpensive.

Yup. You can choose the right template, copy-paste the contents from the word doc and click on auto-format. You'll have a publish-ready paper that you can download at the end.

A matter of seconds. Besides that, our intuitive editor saves a load of your time in writing and formating your manuscript.

One little Google search can get you the Word template for any journal. However, why do you need a Word template when you can write your entire manuscript on SciSpace, autoformat it as per Journal of Cheminformatics's guidelines and download the same in Word, PDF and LaTeX formats? Try us out!.

Absolutely! You can do it using our intuitive editor. It's very easy. If you need help, you can always contact our support team.

SciSpace is an online tool for now. We'll soon release a desktop version. You can also request (or upvote) any feature that you think might be helpful for you and the research community in the feature request section once you sign-up with us.

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After you have written and autoformatted your paper, you can download it in multiple formats, viz., PDF, Docx and LaTeX.

To be honest, the answer is NO. The impact factor is one of the many elements that determine the quality of a journal. Few of those factors the review board, rejection rates, frequency of inclusion in indexes, Eigenfactor, etc. You must assess all the factors and then take the final call.

SHERPA/RoMEO Database

We have extracted this data from Sherpa Romeo to help our researchers understand the access level of this journal. The following table indicates the level of access a journal has as per Sherpa Romeo Archiving Policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

The 5 most common citation types in order of usage are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

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After uploading your paper on SciSpace, you would see a button to request a journal submission service for Journal of Cheminformatics.

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Yes. SciSpace provides this functionality.

After signing up, you would need to import your existing references from Word or .bib file.

SciSpace would allow download of your references in Journal of Cheminformatics Endnote style, according to springer guidelines.

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