MML-DTI: Multimanifold Learning with Hyperbolic Graph Neural Networks for Enhanced Drug-Target Interaction Prediction

Accurately predicting drug-target interactions (DTIs) is crucial for drug discovery, repositioning. However, most deep learning-based DTI models are designed in Euclidean space, making it difficult to effectively represent the hierarchical and scale-free characteristics of biological data. Due to its unique negatively curved geometric properties, hyperbolic space can more effectively represent hierarchical relationships within data. Therefore, we propose a multimanifold learning framework that integrates multimodal features in hyperbolic and Euclidean spaces for drug-target interaction prediction. Specifically, we employ a Hyperbolic Graph Neural Network (HGNN) to extract features from molecular graphs of small-molecular drugs, thereby effectively capturing the hierarchical structural information within these graphs. To integrate heterogeneous information, a Multi-Manifold Feature Fusion Module combines structural features from the HGNN, chemical fingerprints, and semantic embeddings derived from pretrained language models. Extensive experiments on benchmark data sets demonstrate that our framework achieves superior performance compared with state-of-the-art Euclidean-based methods. The experimental results demonstrate that hyperbolic geometry offers significant advantages in extracting hierarchical features from non-Euclidean data and also highlight the promising potential of multimanifold feature fusion in the field of drug-target interaction prediction.