HiGraphDTI: Hierarchical Graph Representation Learning for Drug-Target Interaction Prediction

arXiv:2404.10561v1 Announce Type: new
Abstract: The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and target chemical structures. However, existing deep learning methods typically generate drug features via aggregating molecular atom representations, ignoring the chemical properties carried by motifs, i.e., substructures of the molecular graph. The atom-drug double-level molecular representation learning can not …

abstract arxiv cs.lg deep learning development discovery extract features graph graph representation hierarchical however interactions pharmaceutical prediction q-bio.qm representation representation learning results robust role stat.ml type