Edited by Igor V. Tetko, Helmholtz Zentrum München, Germany
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 collection in Journal of Cheminformatics is to show progress and exemplify the current needs, trends and requirements for machine learning in chemical data analysis. In particular, it focuses 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 collection mainly contains 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.