An inductive graph neural network model for compound-protein interaction prediction based on a homogeneous graph.

ABSTRACT: Identifying the potential compound-protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound-protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds

SUBMITTER: Wan X 

PROVIDER: S-EPMC9310259 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature