ChemHyperMag：基于物理信息的磁超图学习改进分子ADMET预测
ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction
AI总结：
该研究针对分子ADMET预测问题，提出ChemHyperMag方法，通过构建官能团超图、定义势驱动不可逆流等，经磁拉普拉斯编码和磁切比雪夫编码器处理，在多基准实验中以更少样本实现改进，且具有可扩展性和可解释性。
AI中文摘要：
准确预测ADMET（吸收、分布、代谢、排泄和毒性）对药物发现至关重要。大多数预测器使用无向分子图和成对边，忽略了不对称相互作用、不可逆动力学以及官能团和环系统的基序水平效应。我们提出ChemHyperMag用于缺失标签下的多任务ADMET预测。它从环、BRICS片段、Bemis-Murcko支架和键构建官能团超图，定义由电负性和Gasteiger部分电荷引导的势驱动不可逆流，通过厄米磁拉普拉斯编码循环并用磁切比雪夫编码器处理。通过扰动磁相形成随机视图并使用InfoNCE目标训练。多个ADMET基准实验表明，与近期方法相比，在更少标记样本且无构象异构体的情况下有改进。ChemHyperMag可扩展并通过其磁相提供可解释的方向信号。
英文摘要：
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.
