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Upcoming Special Collections

This is a list of calls for papers for current special collections. For an archive of calls for papers, click here

Biomedical Data Analyses Facilitated by Open Cheminformatics Workflows

Modern data science approaches aim to properly interconnect information in order to generate new knowledge and reveal hidden relationships in the data. The open data revolution has given users explicit rights, via open licenses, to download, curate, and reshare results, leading to a democratization of data. Research environments can make use of large publicly available data sets in the domain of life sciences. For example, the impact of the CC-BY-SA licensing of ChEMBL, funded by the Wellcome Trust, has boosted the compound-target interaction modelling [1].

Data sets - from small molecules to new modalities, such as peptides and oligonucleotides among others - require careful data curation (including data integration, annotation, filtering, and standardization) before they can be used for purposes such as data analysis, visualization, or predictive modeling. It is recommended to generate reusable workflows to avoid tedious repetitive data curation tasks in the future. Furthermore, making scripts for data curation and analyses freely available to the scientific community is a fundamental component for comparability of research and of reproducible Open Science, a core vision of the Journal of Cheminformatics.

Studies analyzing biomedical data have gained interest through the ever increasing amount and diversity of publicly available life science data sets and can provide valuable insights into quality and composition of data sets, biases and trends of the data etc.

For this Special Collection we welcome contributions focusing on - but not limited to - cheminformatics workflows (such as Jupyter notebooks, RMarkdown, Common Workflow Language, Galaxy, KNIME workflows etc.) licensed with an OSI-approved [2] or Creative Commons license (CCZero, CC-BY, CC-BY-SA, but not ND and NC) [3], serving the curation and analysis of diverse life science data sets. The collection will highlight the need for automation, transparency, and re-usability of cheminformatics workflows in drug discovery and related fields, lower the barrier for effective usage of reproducible workflows, and should lay a basis for community-wide standards in the domain of data curation and analysis. The usability of the workflow(s) should be demonstrated on basis of (a) publicly available / open licensed data set(s).

The collection welcomes original papers and encourages the submission of educational papers / tutorials. We also encourage authors to participate in the pilot on using Citation Typing Ontology (CiTO) [4].

As for any research article, Journal of Cheminformatics will only publish research or software that is entirely reproducible by third parties. See detailed submission guidelines here.

References

  1. https://wellcome.org/press-release/open-access-drug-discovery-database-launches-half-million-compounds
  2. https://opensource.org/about
  3. https://creativecommons.org/
  4. https://www.biomedcentral.com/collections/cito

Keywords

Data analyses, data curation, data mining, open science, open data, drug discovery, cheminformatics workflows

Submission Deadline

31 October 2021

Guest Editors

Barbara Zdrazil

University of Vienna, Austria

Eva Nittinger

Department of Medicinal Chemistry, Respiratory and Immunology, AstraZeneca, Sweden

Alex Clark

Research Informatics, Collaborative Drug Discovery, Inc., Canada

Anna Gaulton

European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), UK

In Silico Structure Generation: Recent Developments, Applications, and Challenges

Chemical structures are at the core of the research in chemistry. In several applications, the chemical structures available in the physically exemplified chemical space are used. This includes virtual and “on-demand” libraries populated by chemical structures already constructed but not synthesized yet. However, it is well-known that the chemical space is huge and there is an increasing need to automatically generate novel chemical structures. Such need is evident in areas such as drug discovery, metabolomics, and planned organic synthesis.

The main objective of this special collection in the Journal of Cheminformatics is to show recent advances, applications, challenges in the enumeration of chemical structures: from the design to the analysis and use of either small, focused data sets, to large compound libraries. Analysis and handling of the newly constructed chemical structures include the storage, mining, integration of the constructed structures with other existing data sets, and curation.

The collection welcomes original papers and encourages the submission of tutorials (outlined in the recent Editorial "Learning cheminformatics") to foster the interest of the journal to contribute to the education of cheminformatics.

As any research article, Journal of Cheminformatics will only publish research or software that is entirely reproducible by third parties. See full and detail submission guidelines here.

Keywords

Structure enumeration, drug discovery, metabolomics, data mining, chemical space, synthesis design, high-throughput screening, virtual and on-demand libraries

Submission deadline

31 December 2020

Guest Editors

José L. Medina-Franco

School of Chemistry
National Autonomous University of Mexico (UNAM)

Emma Schymanski

Luxembourg Centre for Systems Biomedicine (LCSB)
University of Luxembourg

Christoph Steinbeck

Friedrich-Schiller-University Jena
Germany

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