A Survey of Multi-Task Learning Methods in Chemoinformatics

Авторы: Sosnin Sergey, Vashurina Mariia, Withnall Michael, Karpov Pavel, Fedorov Maxim, Tetko Igor V.
Журнал: Molecular informatics
Том: 37
Год издания: 2018
Издательство: Wiley-VCH Verlag
Местоположение издательства: Weinheim, Germany, Germany
DOI: 10.1002/minf.201800108
Аннотация: 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.
Добавил в систему: Соснин Сергей Борисович

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