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Deep Learning in Drug Discovery
Author(s) -
Gawehn Erik,
Hiss Jan A.,
Schneider Gisbert
Publication year - 2016
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201501008
Subject(s) - drug discovery , deep learning , cheminformatics , computer science , artificial intelligence , informatics , convolutional neural network , field (mathematics) , data science , artificial neural network , deep neural networks , machine learning , bioinformatics , engineering , biology , mathematics , pure mathematics , electrical engineering
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.