Deep Learning in Drug Discovery
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”. Com...
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| Published in | Molecular informatics Vol. 35; no. 1; pp. 3 - 14 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Weinheim
WILEY-VCH Verlag
01.01.2016
WILEY‐VCH Verlag Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1868-1743 1868-1751 1868-1751 |
| DOI | 10.1002/minf.201501008 |
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| 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. |
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| AbstractList | 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.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. 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. |
| Author | Schneider, Gisbert Gawehn, Erik Hiss, Jan A. |
| Author_xml | – sequence: 1 givenname: Erik surname: Gawehn fullname: Gawehn, Erik organization: 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 – sequence: 2 givenname: Jan A. surname: Hiss fullname: Hiss, Jan A. organization: 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 – sequence: 3 givenname: Gisbert surname: Schneider fullname: Schneider, Gisbert email: gisbert.schneider@pharma.ethz.ch organization: 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 |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/27491648$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Artificial Intelligence bioinformatics cheminformatics Computational Biology - methods drug design Drug Discovery - methods Humans Informatics Machine Learning neural network Neural networks Neural Networks (Computer) Pharmaceutical industry Proteomics - methods virtual screening |
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| Title | Deep Learning in Drug Discovery |
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