MolGraph-xLSTM as a graph-based dual-level xLSTM framework for enhanced molecular representation and interpretability.

ABSTRACT: Predicting molecular properties is essential for drug discovery, and computational methods can greatly enhance this process. Molecular graphs have become a focus for representation learning, with Graph Neural Networks (GNNs) widely used. However, GNNs often struggle with capturing long-range dependencies. To address this, we propose MolGraph-xLSTM, a novel graph-based xLSTM model that enhances feature extraction and effectively models molecule long-range interactions. Our approach processes molecular graphs at two scales: atom-level and motif-level. For atom-level graphs, a GNN-based xLSTM framework with jumping knowledge extracts local features and aggregates multilayer information to capture both local and global patterns effectively. Motif-level graphs provide complementary structural i

SUBMITTER: Sun Y 

PROVIDER: S-EPMC12480901 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature