A survey of multi-task learning methods in chemoinformatics

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Sosnin, Sergey and Vashurina, Mariia and Withnall, Michael and Karpov, Pavel and Fedorov, Maxim and Tetko, Igor V. (2018) A survey of multi-task learning methods in chemoinformatics. Molecular Informatics. ISSN 1868-1751 (https://doi.org/10.1002/minf.201800108)

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Abstract

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.

  • Item type:Article
    ID code:66444
    Dates:
    Date
    Event
    28 November 2018
    Published
    28 November 2018
    Published Online
    16 October 2018
    Accepted
    Subjects:Science > Physics
    Science > Chemistry
    Department:Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:21 Dec 2018 11:37
    Last modified:03 Dec 2024 19:25
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/66444
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