A survey of multi-task learning methods in chemoinformatics
Sosnin, Sergey and Vashurina, Mariia and Withnall, Michael and Karpov, Pavel and Fedorov, Maxim and Tetko, Igor V. (2018) A survey of multi-task learning methods in chemoinformatics. Molecular Informatics. ISSN 1868-1751 (https://doi.org/10.1002/minf.201800108)
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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.
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Item type: Article ID code: 66444 Dates: DateEvent28 November 2018Published28 November 2018Published Online16 October 2018AcceptedSubjects: Science > Physics
Science > ChemistryDepartment: Faculty of Science > Physics Depositing user: Pure Administrator Date deposited: 21 Dec 2018 11:37 Last modified: 11 Nov 2024 12:10 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/66444