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 extended abstract to the ICANN2019 is April 1st, 2019
The deadline for submission of articles to this issue is September 17th, 2019.
Edited by Igor V. Tetko