This approach provides an interpretable AI strategy for uncovering the communication mechanisms that underlie folding, allostery, and long-range signal propagation in molecular machines, while also supporting AI-guided modulation of biomolecular function and rational protein engineering.
Graph neural networks have shown strong potential for learning structural representations of biological networks. However, repeated message passing may blur local structural signals that are relevant for motif- and graphlet-based analysis. This paper investigates multilabel graphlet classification in protein–protein in...
Lidija Kunst, Friedhelm Schwenker, H. Kestler· Entropy· 0 citations
Molecular property prediction is central to computational drug discovery, where graph neural networks learn to weight neighboring atomic environments during message passing. Yet it remains unclear how variational quantum circuits alter learned attention behavior in molecular graphs. We introduce an edge-aware variation...
Yu-Cheng Lin, Yu-Chao Hsu, Tai-Yue Li et al.· 0 citations
Directed graphs naturally model many real-world systems in which interactions are asymmetric, such as citation networks, web graphs, and information-flow networks. However, graph learning methods commonly rely on message passing with symmetrized graph representations or positional encodings that only partially exploit...
Lydia Mezrag, Semih Cantürk, Michael Perlmutter et al.· 0 citations
Graph Neural Networks (GNNs) are powerful tools for learning on relational data, but many real-world graphs evolve continuously, and existing Dynamic GNNs (DGNNs) often process large portions of the graph at every time step. In many temporal graphs, change is local: only a small, shifting subset of nodes is active at a...
Md. Redwan Islam, Pardis Sadatian Moghaddam, I. B. Arpinar· IEEE Access· 0 citations
This paper presents an engineering-oriented study of edge-aware graph neural network baselines for protein function prediction on the OGBN-Proteins benchmark. The benchmark represents a large protein-protein interaction graph whose edges carry eight-dimensional association evidence and whose proteins have 112 functiona...
Aleksandar Stankovic, Dejan Lisica· IJEEC - INTERNATIONAL JOURNA...· 0 citations
