A survey of multi-task learning methods in chemoinformatics

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

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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.

Original languageEnglish
Number of pages11
JournalMolecular Informatics
Early online date28 Nov 2018
DOIs
Publication statusE-pub ahead of print - 28 Nov 2018

Keywords

  • multi-task learning
  • neural networks
  • transfer learning

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  • Cite this

    Sosnin, S., Vashurina, M., Withnall, M., Karpov, P., Fedorov, M., & Tetko, I. V. (2018). A survey of multi-task learning methods in chemoinformatics. Molecular Informatics. https://doi.org/10.1002/minf.201800108