A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale.

ABSTRACT: Biomedical network learning offers fresh prospects for expediting drug repositioning. However, traditional network architectures struggle to quantify the relationship between micro-scale drug spatial structures and corresponding macro-scale biomedical networks, limiting their ability to capture key pharmacological properties and complex biomedical information crucial for drug screening and therapeutic discovery. Moreover, challenges such as difficulty in capturing long-range dependencies hinder current network-based approaches. To address these limitations, we introduce the Spatial Hierarchical Network, modeling molecular 3D structures and biological associations into a unified network. We propose an end-to-end framework, SpHN-VDA, integrating spatial hierarchical information through tripl

SUBMITTER: Ren Z 

PROVIDER: S-EPMC11525566 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature