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A Survey of Multi‐task Learning Methods in Chemoinformatics
Author(s) -
Sosnin Sergey,
Vashurina Mariia,
Withnall Michael,
Karpov Pavel,
Fedorov Maxim,
Tetko Igor V.
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201800108
Subject(s) - cheminformatics , chemical space , computer science , task (project management) , machine learning , virtual screening , data mining , representation (politics) , artificial intelligence , data science , drug discovery , bioinformatics , engineering , systems engineering , politics , law , political science , biology
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.