Günter Klambauer, Sepp Hochreiter, and Matthias Rarey
Machine learning is a working horse of modern drug discovery and has been ever since the early days of QSAR. Despite this long tradition, machine learning methods gained substantial momentum recently triggered by the success of deep learning in many application areas. A wide range of tasks in modeling and cheminformatics have been influenced by machine learning, such as chemical synthesis planing and library design, bioactivity and toxicity prediction, and virtual screening. Machine learning methods are no more restricted to traditional data types, such as compounds and protein sequences, but also extend to protein structures, imaging, textual, and transcriptomics data. We are pleased to see that the works included in this special issue reflect this variety of tasks, data types, and methods. From the methodological point of view, the most notable effect is the tendency toward deep learning and deep neural networks.
Machine learning, especially deep learning, has become an even more important and fruitful basis for computational methods in drug discovery as reflected by the recent successes in this field. Looking back to the issues of this journal over the past 12 months when we started to think about this special issue, the growing role of machine learning cannot be overlooked. The editors hope that this issue brings these two fields closer together, encourages collaborations between machine learning groups and computational and medicinal chemists, enables newcomers to get an introduction, and provides an overview of the current research status for experts.
We envision that employing machine learning in drug discovery will eventually lead to new, more efficient, and safer drugs.
Journal of Chemical Information and Modeling , 59, 3, 945, 2019-03-25.