Journal
MOLECULAR INFORMATICS
Volume 38, Issue 4, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/minf.201800108
Keywords
Multi-task learning; transfer learning; neural networks
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Funding
- European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie grant [676434]
- Russian Science Foundation [14-43-00024]
- ERA-NET on Cardio Vascular Diseases (ERA-CVD) Cardio-Oncology Project
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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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