A Survey of Multi‐task Learning Methods in Chemoinformatics

@article{Sosnin2019ASO,
  title={A Survey of Multi‐task Learning Methods in Chemoinformatics},
  author={S. Sosnin and M. Vashurina and M. Withnall and Pavel Karpov and Maxim V. Fedorov and I. Tetko},
  journal={Molecular Informatics},
  year={2019},
  volume={38}
}
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… Expand

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