SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions.

ABSTRACT:

Motivation

Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information.

Results

In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism

SUBMITTER: Li X 

PROVIDER: S-EPMC10818136 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature