title: Deep Learning in Drug Discovery.
abstract: Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.
authors: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
authors: Erik
authors: E
authors: Gawehn
authors: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.
authors: Jan A
authors: JA
authors: Hiss
authors: Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38. gisbert.schneider@pharma.ethz.ch.
authors: Gisbert
authors: G
authors: Schneider
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D040901:Proteomics / Q000379:methods
keywords: bioinformatics; cheminformatics; drug design; machine-learning; neural network; virtual screening