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