INGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models.

ABSTRACT:

Motivation

Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization.

Results

We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph n

SUBMITTER: Sun Y 

PROVIDER: S-EPMC10957515 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature