A Survey of Multi-task Learning Methods in Chemoinformatics.
Multi-task learning
neural networks
transfer learning
Journal
Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315
Informations de publication
Date de publication:
04 2019
04 2019
Historique:
received:
27
08
2018
accepted:
16
10
2018
pubmed:
1
12
2018
medline:
30
5
2019
entrez:
1
12
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30499195
doi: 10.1002/minf.201800108
pmc: PMC6587441
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1800108Informations de copyright
© 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.
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