Molecular machine learning (ML) underpins critical workflows in drug discovery, material science, and catalyst optimization by rapidly predicting molecular interactions and properties. For instance, ...

Juan Casado Cordón, Professor of Physical Chemistry at the University of Malaga, considers graphene—an infinite layer of ...

At ARVO 2025, in Salt Lake City, Utah, Patipol Tiyajamorn, talked about his poster on using graph neural networks to identify ...

Neo4j Inc. today announced a new serverless offering that dramatically simplifies the deployment of its graph database offering, making it easier to use with artificial intelligence applications.

Abstract: Recently, graph neural architecture search (GNAS) frameworks have been successfully used to automatically design the optimal neural architectures for many problems such as node ...

This paper proposes TarMGDif, a novel target-specific molecular graph generation model based on a discrete denoising diffusion framework which could handle graph structure. TarMGDif incorporates a ...

Lung cancer remains a leading cause of global cancer mortality, demanding precise diagnostic tools for accurate subtype classification. This paper introduces a novel Enhanced GraphSAGE (E-GraphSAGE) ...

For these GDB molecules, we find that an increasing fraction (MC1) or number (MC2) of non-divalent nodes in the molecular graph represent simple measures of molecular complexity, which we interpret in ...

In this paper, we propose an end-to-end framework for EEG classification that integrates power spectral density (PSD) and visibility graph (VG) features together with deep learning (DL) techniques.

Introduction: 3D Geometric Graph Neural Networks (GNNs) have emerged as transformative tools for modeling molecular data. Despite their predictive power, these models often suffer from limited ...