Big Data and Deep Learning: A New Age of Molecular Informatics?
Knut Baumann, Gisbert Schneider · Molecular Informatics · 2017
As a scientific researcher one might feel invigorated reading about the recent excitement caused by articles on computational “Big Data” analysis and “Deep Learning” methods. A world of seemingly endless new opportunities for computer applications seems to have suddenly opened up. Without doubt, there is a continuously increasing demand for computational project support in the life sciences. Still, one may wonder if all of the perceived enthusiasm is realistic. A healthy skepticism seems appropriate when reading some of the articles available on the topic. The readers of Molecular Informatics will have noted that we have, as one of the scientific first journals, published several seminal papers on this topic, and we intend to strengthen our trend-setting position. Thorough analyses of both the algorithms and their applications, together with a critical assessment of the opportunities and challenges of this emerging field of research, seem imperative. Advanced machine learning methods have become mainstream in computational drug discovery. This is reflected by the continuously increasing number of publications in this area. Molecular Informatics is committed to provide the most appropriate publication forum for these interdisciplinary research studies. We therefore invite you to submit contributions to Molecular Informatics on the topics of big data and deep learning methods and their applications. Since 2012, the “Molecular Informatics Best Paper Award” has been given to distinguish excellent publications in our journal. It is an annual award reflecting the development of the field, and it is based on the editors' choice, taking into account criteria such as novelty, topic, online usage, and impact. We are delighted to announce that the Editor's choice for the 2016 “Molecular Informatics Best Paper Award” goes to the paper entitled “Predictive Models for Halogen-bond Basicity of Binding Sites of Polyfunctional Molecules” led by Alexandre Varnek, from the Laboratoire de Chémoinformatique at Université de Strasbourg, France.1 Congratulations to the whole team of authors of this work! We would like to express our gratitude to all authors, peer reviewers, and Editorial Advisory Board members who contributed to Molecular Informatics during the past year and help us to keep its high standards. We are particularly grateful to Gerhard Ecker and Jordi Mestres, who, having served as dedicated editors of Molecular Informatics for many years, decided to make place for a renewed team of editors. We are delighted to welcome Sourav Das (St. Jude Children's Research Hospital, Memphis, TN, USA) and Yoshihiro Yamanishi (Kyushu University, Japan) as Associate Editors. Molecular Informatics has become a leading journal covering all aspects of chem- and bioinformatics, and we will continue to work towards strengthening the journal's position in our community.