Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery

Abstract

Circular RNAs (circRNAs) play pivotal roles in various biological processes and disease progression, particularly in modulating drug responses and resistance mechanisms. Accurate prediction of circRNA-drug associations (CDAs) is essential for biomarker discovery and the advancement of therapeutic strategies. Although several computational approaches have been proposed for identifying novel circRNA therapeutic targets, their performance is often limited by inadequate modeling of higher-order geometric information within circRNA-drug interaction networks. To overcome these challenges, we propose G2CDA, a geometric graph representation learning framework specifically designed to enhance the identification of CDAs and facilitate therapeutic target discovery. G2CDA introduces torsion-based geometric encoding into the message propagation process of the circRNA-drug network. For each potential association, we construct local simplicial complexes, extract their geometric features, and integrate these features as adaptive weights during message propagation and aggregation. This design promotes a richer understanding of local topological structures, thereby improving the robustness and expressiveness of learned circRNA and drug representations. Extensive benchmark evaluations on public datasets demonstrate that G2CDA outperforms state-of-the-art CDA prediction models, particularly in identifying novel associations. Case studies further confirm its effectiveness by uncovering potential drug interactions with the ALDH3A2 and ANXA2 biomarkers. Collectively, G2CDA provides a robust and interpretable framework for accelerating circRNA-based therapeutic target discovery and streamlining drug development pipelines. Our code are archived in: https://github.com/lizhen5000/G2CDA.

Keywords: biomarker discovery; circRNA therapeutic targets; circRNA-drug network; drug development; geometric graph representation.

FIGURE 1

G2CDA’s architecture, comprising: (A) construction of the circRNA-drug graph, (B) extracting CDA-centered subgraphs, (C) calculating CDA torsion, (D) integrating geometric message propagation, and (E) training and inference.

FIGURE 2

Model performance at different GNN layers.

FIGURE 3

Model performance at different hidden layer dimensions.

FIGURE 4

Model performance at different output layer dimensions.

FIGURE 5

Performance comparison across initialization methods.

FIGURE 6

Comparison of GNN models with and without Torsion (“w/t” refers to the model without the “Torsion” component).