ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
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Computer Science > Machine Learning
arXiv:2607.18332 (cs)
[Submitted on 19 Jul 2026]
Title:ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
Authors:Hexiao Ding, Hongzhao Chen, Jing Lan, Yufeng Jiang, Zihong Luo, Zehua Xiong, Tianlong Ruan, Yunlin Mao, Nga Chun Ng, Gwing Kei Yip, Gerald W.Y. Cheng, Kate Inyoung Oh, Jing Cai, Liang-Ting Lin, Jung Sun Yoo
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Abstract:Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predictors use undirected molecular graphs and pairwise edges. This choice misses asymmetric interactions, nonreversible dynamics, and motif level effects from functional groups and ring systems. We propose ChemHyperMag for multitask ADMET prediction under missing labels. ChemHyperMag builds a functional group hypergraph from rings, BRICS fragments, Bemis-Murcko scaffolds, and bonds. It also defines a potential driven nonreversible flow guided by electronegativity and Gasteiger partial charges. The resulting circulation is encoded by a Hermitian magnetic Laplacian and processed with a magnetic Chebyshev encoder. We perturb magnetic phases to form stochastic views and train with an InfoNCE objective. Experiments on multiple ADMET benchmarks show improvements over recent methods with fewer labeled samples and no conformers. ChemHyperMag is scalable and provides interpretable directional signals through its magnetic phases.
| Comments: | Accepted by Proceedings of the AI4Physics Workshop at the 43 rd International Conference on Machine Learning (AI4Physics@ICML 2026) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.18332 [cs.LG] |
| (or arXiv:2607.18332v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.18332
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction, by Hexiao Ding and Hongzhao Chen and Jing Lan and Yufeng Jiang and Zihong Luo and Zehua Xiong and Tianlong Ruan and Yunlin Mao and Nga Chun Ng and Gwing Kei Yip and Gerald W.Y. Cheng and Kate Inyoung Oh and Jing Cai and Liang-Ting Lin and Jung Sun Yoo
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