DeepNC: a framework for drug-target interaction prediction with graph neural networks.

ABSTRACT: The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-bas

SUBMITTER: Tran HNT 

PROVIDER: S-EPMC9107302 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature