Issue 23, 2020

Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks

Abstract

Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations – the cost of representation generation, risk of inherited bias, and the requirement for large amounts of data. We propose the use of multi-task learning in tandem with transfer learning to address these limitations directly. In order to avoid introducing unknown bias into multi-task learning through the task selection itself, we calculate task similarity through pairwise task affinity, and use this measure to programmatically select tasks. We test this methodology on several real-world data sets to demonstrate its potential for execution in complex and low-data environments. Finally, we utilise the task similarity to further probe the expressiveness of the learned representation through a comparison to a commonly used cheminformatics fingerprint, and show that the deep representation is able to capture more expressive task-based information.

Graphical abstract: Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks

Supplementary files

Article information

Article type
Paper
Submitted
29 ⵉⴱⵔ 2020
Accepted
26 ⵎⴰⵢ 2020
First published
28 ⵎⴰⵢ 2020

Phys. Chem. Chem. Phys., 2020,22, 13041-13048

Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks

C. Fare, L. Turcani and E. O. Pyzer-Knapp, Phys. Chem. Chem. Phys., 2020, 22, 13041 DOI: 10.1039/D0CP02319A

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