A Survey of Multi-task Learning Methods in Chemoinformatics.


Journal

Molecular informatics
ISSN: 1868-1751
Titre abrégé: Mol Inform
Pays: Germany
ID NLM: 101529315

Informations de publication

Date de publication:
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

e1800108

Informations de copyright

© 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

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Auteurs

Sergey Sosnin (S)

Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology Skolkovo Innovation Center, Moscow, 143026, Russia.

Mariia Vashurina (M)

Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany.

Michael Withnall (M)

Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany.

Pavel Karpov (P)

Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany.

Maxim Fedorov (M)

Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology Skolkovo Innovation Center, Moscow, 143026, Russia.
University of Strathclyde, Department of Physics John Anderson Building, 107 Rottenrow East, G40NG, Glasgow, United Kingdom.

Igor V Tetko (IV)

Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-85764, Neuherberg, Germany.
BIGCHEM GmbH, Ingolstädter Landstraße 1, b. 60w, D-85764, Neuherberg, Germany.

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Classifications MeSH