AI diffusion models tailor drug molecules to custom-fit protein targets, speeding drug development and evaluation

University of Virginia School of Medicine scientists have developed a bold new approach to drug development and discovery ...
EurekAlert!

Heterogeneous spatio-temporal graph contrastive learning for point-of-interest recommendation

As one of the most crucial topics in the recommendation system field, Point-of-Interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks ...
EurekAlert!

Node-centric expression models (NCEMs): Graph-neural networks reveal communication between cells

How do single cells communicate in a tissue? How can these interactions be modeled, while retaining information of spatial context? Researchers around Fabian Theis from Helmholtz Munich Computational ...
Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
Finextra

Graph Neural Networks and GraphRAG: Navigating Open-World Complexity in Finance

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...
Semiconductor Engineering

Physics Simulation With Graph Neural Networks Targeting Mobile

The demand for immersive, realistic graphics in mobile gaming and AR or VR is pushing the limits of mobile hardware. Achieving lifelike simulations of fluids, cloth, and other materials historically ...