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      Learning cheminformatics

      editorial
      1 , ,   2
      Journal of Cheminformatics
      Springer International Publishing

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          Abstract

          Since its inception, the Journal of Cheminformatics has made it a point to highlight novel approaches and best practices in cheminformatics. However, one area that we have not addressed are articles on educational aspects of cheminformatics. Given the increasing importance of chemical information throughout the practice of chemistry, biology and related fields, we realize that articles that focus on introductory aspects of chemical information and cheminformatics would be beneficial to newcomers to the field, which can range from undergraduate students to experienced chemists coming from experimental subfields of chemistry. But we need not restrict ourselves to “introductory” articles. For example, tutorial style articles that describe a common workflow, using a set of open source tools could be generally useful and would be considered within the scope educational article type. We note that the ACS Journal of Chemical Education (ISSN:0021-9584) provides a venue for educational articles in chemistry, and while some cheminformatics related articles have been published (e.g. [1, 2]), we feel that the Journal of Cheminformatics provides a more focused context for cheminformatics specific educational material. In particular, we have developed a set of guidelines for manuscripts that are intented for the educational category which can be viewed at https://jcheminf.biomedcentral.com/submission-guidelines/preparing-your-manuscript/educational. We are interested in both expository style and tutorial style articles. The former can include articles that present a a pedagogical view of a pre-existing topic, such as chemical similarity or SMILES & SMARTS for pattern matching. Other topics could focus on aspects of teaching cheminformatics in a classroom. In particular descriptions of pedagogical experiments with chemical information curricula will be of interest. Tutorial style articles are also of interest and are meant to be step by step descriptions of specific types of calculations—for example, the article by Voicu et al. [3], describe the use of the rcdk R package [4] to compute a clustering of a compound collection. While this task is a standard operation, it is useful for novices to see the whole process described explicitly from start to end. A key requirement for educational articles is that they employ freely accessible tools and data, to ensure that the material can be followed by readers with minimal limitations. This is in line with requirements for other article types, but for educational articles we plan on requiring the use of open source tools and reusable data with an open license. In summary, we believe that there is a need for the educational article type and that such articles will enhance the value of the journal to its readership and more broadly within the cheminformatics community.

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          Most cited references4

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          ChemSpider: An Online Chemical Information Resource

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            Chemical Informatics Functionality inR

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              The rcdk and cluster R packages applied to drug candidate selection

              The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster. We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a “score” obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies. Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward ’s method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.
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                Author and article information

                Contributors
                rajarshi_guha@vrtx.com
                egon.willighagen@maastrichtuniversity.nl
                Journal
                J Cheminform
                J Cheminform
                Journal of Cheminformatics
                Springer International Publishing (Cham )
                1758-2946
                20 January 2020
                20 January 2020
                2020
                : 12
                : 4
                Affiliations
                [1 ]ISNI 0000 0004 0384 7506, GRID grid.422219.e, Vertex Pharmaceuticals, ; 50 Northern Ave, Boston, MA 02210 USA
                [2 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Maastricht University, ; Universiteitssingel 50, 6229 ER  Maastricht, The Netherlands
                Author information
                http://orcid.org/0000-0001-7403-8819
                http://orcid.org/0000-0001-7542-0286
                Article
                406
                10.1186/s13321-019-0406-z
                6970289
                8101e602-6ce9-4ff0-8ebf-25599e468f44
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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                © The Author(s) 2020

                Chemoinformatics
                Chemoinformatics

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