A Survey of Multi‐task Learning Methods in Chemoinformatics. Issue 4 (28th November 2018)

• Record Type: Journal Article
• Authors: Sosnin, Sergey; Vashurina, Mariia; Withnall, Michael; Karpov, Pavel; Fedorov, Maxim; Tetko, Igor V.
• Is Part Of: Molecular informatics. Volume 38:Issue 4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-28
• Subjects: Multi-task learning -- transfer learning -- neural networks; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201800108
• Languages: English
• ISSNs: 1868-1743

Abstract

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. Abstract :