ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction

AI 中文解读

ChemHyperMag来了！这次科学家给AI装上“磁力眼”，能更精准预测药物分子在人体内的吸收、分布、代谢、排泄和毒性——也就是ADMET性质。以前的方法只把分子当成简单的球棍模型，忽略了官能团之间的非对称互动和环状结构的影响，就像只看了交通路线图却不管车流方向。ChemHyperMag巧妙构建了“功能团超图”，并引入电负性驱动的不可逆流动，用磁性拉普拉斯算子捕捉方向信号，即使样本数据少也能给出靠谱预测。这对普通人意味着什么？新药研发周期可能大幅缩短，成本降低，未来你吃的药会更安全、副作用更少——AI在试管和临床之间架起了一座更聪明的桥梁。

arXiv:2607.18332v1 Announce Type: new 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.