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A survey of multi-task learning methods in chemoinformatics.
Mol. Inform. 37:1800108 (2018)
Despite the increasing volume of available data, the proportion of experimentally measured data remains small compared to the virtual chemical space of possible chemical structures. Therefore, there is a strong interest in simultaneously predicting different ADMET and biological properties of molecules, which are frequently strongly correlated with one another. Such joint data analyses can increase the accuracy of models by exploiting their common representation and identifying common features between individual properties. In this work we review the recent developments in multi-learning approaches as well as cover the freely available tools and packages that can be used to perform such studies.
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Publication type
Article: Journal article
Document type
Review
Keywords
Multi-task Learning ; Transfer Learning ; Neural Networks; Toxicity Prediction; Neural-networks; Regression; Model; Gtm
ISSN (print) / ISBN
1868-1743
e-ISSN
1868-1751
Journal
Molecular Informatics
Quellenangaben
Volume: 37,
Issue: 4,
Article Number: 1800108
Publisher
Wiley
Publishing Place
Weinheim
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)