Prediction of drug-target interactions through multi-task learning.

ABSTRACT: Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. However, multi-task learning sometimes worsens the overall performance or generates a trade-off between individual task's performance. In this study, we propose a general multi-task learning scheme that not only increases the average performance but also minimizes individual performance degradation, through group selection and knowledge distillation. The groups are selected on the basis of chemical similarity between ligand sets of targets, and the similar targets in the same groups are trained together. During training, we apply knowledge distil

SUBMITTER: Moon C 

PROVIDER: S-EPMC9622881 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature