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arXiv · 2506.11041

ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery

Xiaobao Huang·Yihong Ma·Anjali Gurajapu·Jules Schleinitz·Zhichun Guo·Sarah E. Reisman·Nitesh V. Chawla
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Abstract

Reaction virtual screening and discovery are fundamental challenges in chemistry and materials 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.

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Xiaobao Huang, Yihong Ma, Anjali Gurajapu, Jules Schleinitz, Zhichun Guo, Sarah E. Reisman, Nitesh V. Chawla. 2025-05-21. ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery. https://arxiv.org/abs/2506.11041

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