Shiyi Chen, Daniel Martínez, et al.
Physics of Fluids
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.
Shiyi Chen, Daniel Martínez, et al.
Physics of Fluids
J.K. Gimzewski, T.A. Jung, et al.
Surface Science
Tim Erdmann, Stefan Zecevic, et al.
ACS Spring 2024
Ming L. Yu, Lisa A. DeLouise
Surface Science Reports