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

Guest Editors

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methodologies and approaches for their analysis. Artificial Intelligence (AI) and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data.

 The goal of this special issue is to show progress and exemplify the current needs, trends and requirements for machine learning in chemical data analysis. In particular it will focus on the use of chemical informatics and machine learning methodologies to analyse chemical Big Data, e.g. to predict biological activities and physico-chemical properties, facilitate property-oriented data mining, predict biological targets for compounds on a large scale, design new chemical compounds, and analyse large virtual chemical spaces.

 The issue will mainly contain a selection of articles to be presented during the BIGCHEM special session of the International Conference on Artificial Neural Networks (ICANN2019), which is co-organized by the European Neural Network Society and the Horizon2020 Marie Skłodowska-Curie Innovative Training Networks European Industrial Doctorate “Big Data in Chemistry” project. The papers will be published as an article collection in Journal of Cheminformatics – all papers published in the journal are published immediately as open-access articles under a CC-BY license, with copyright held by the authors.

 The submission to this issue will be open until the start of the conference in September 2019. Prospective authors who will submit and present their studies at ICANN2019 are eligible for a 25% discount on the journal’s article-processing charge. For more information on this, please contact either the journal or the issue organiser.

The deadline for submission of an extended abstract to ICANN2019 has passed, and it is no longer possible to sumit an abstract to the conference.

The deadline for submission of full text articles to this issue is December 31st, 2019.

 Edited by Igor V. Tetko