A Survey of Multi-task Learning Methods in Chemoinformatics

Sergey Sosnin, Mariia Vashurina, Michael Withnall, Pavel Karpov, Maxim Fedorov, Igor V. Tetko

    Research output: Contribution to journalReview articlepeer-review

    34 Citations (Scopus)


    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.

    Original languageEnglish
    Article number1800108
    JournalMolecular Informatics
    Issue number4
    Publication statusPublished - Apr 2019


    • Multi-task learning
    • neural networks
    • transfer learning


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