Title: ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Reaction Virtual Screening, Reaction Discovery, Hypergraph Neural Network, Machine Learning

Abstract: Reaction virtual screening and discovery are fundamental challenges in chemistry and material science, where traditional graph neural networks (GNNs) struggle to model multi-reactant interactions.  In this work, we propose ChemHGNN, a hypergraph neural network (HGNN) framework that effectively captures high-order relationships in reaction networks. Unlike GNNs, which require constructing complete graphs for multi-reactant reactions, ChemHGNN naturally models multi-reactant reactions through hyperedges, enabling more expressive reaction representations. To address key challenges—such as combinatorial explosion, model collapse, and chemically invalid negative samples—we introduce a reaction center-aware negative sampling strategy (RCNS) and a hierarchical embedding approach combining molecule, reaction and hypergraph level features. Experiments on the USPTO dataset demonstrate that ChemHGNN significantly outperforms HGNN and GNN baselines, particularly in large-scale settings, while maintaining interpretability and chemical plausibility. Our work establishes HGNNs as a superior alternative to GNNs for reaction virtual screening and discovery, offering a chemically informed framework for accelerating reaction discovery.

TL;DR: This paper introduces ChemHGNN, a hierarchical hypergraph neural network designed to improve virtual screening and discovery of new chemical reactions by modeling multi-reactant interactions more effectively than traditional GNNs.