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Sosnin, S.* ; Vashurina, M. ; Withnall, M.D. ; Karpov, P. ; Fedorov, M.* ; Tetko, I.V.

A survey of multi-task learning methods in chemoinformatics.

Mol. Inform. 37:1800108 (2018)
Postprint DOI PMC
Open Access Green
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Multi-task Learning ; Transfer Learning ; Neural Networks; Toxicity Prediction; Neural-networks; Regression; Model; Gtm
Sprache englisch
Veröffentlichungsjahr 2018
HGF-Berichtsjahr 2018
ISSN (print) / ISBN 1868-1743
e-ISSN 1868-1751
Zeitschrift Molecular Informatics
Quellenangaben Band: 37, Heft: 4, Seiten: , Artikelnummer: 1800108 Supplement: ,
Verlag Wiley
Verlagsort Weinheim
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)
POF Topic(s) 30203 - Molecular Targets and Therapies
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503000-001
DOI 10.1002/minf.201800108
WOS ID WOS:000464983900005
Scopus ID 85057542463
PubMed ID 30499195
Erfassungsdatum 2018-12-03
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