arXiv:2608.21892physics.chem-phcs.LG2026-08

用物理约束模型直接从3D结构预测手性分子的圆二色谱,速度快且结果可解释。

PhysECD: A Physics-Constrained E(3)-Equivariant Framework for Electronic Circular Dichroism Spectrum Prediction

论文配图:PhysECD: A Physics-Constrained E(3)-Equivariant Framework for Electronic Circular Dichroism Spectrum Prediction
图 1 · 摘自论文原文
  • 基于E(3)等变框架,直接预测激发能与电/磁跃迁偶极矩
  • 在CMCDS数据集上平均相关系数达0.642,显著优于以往方法
  • 模型具备手性对称性,适合实时确定分子绝对构型

电子圆二色(ECD)光谱是确定手性分子绝对构型的主要实验手段,但解析需依赖耗时数小时的时变密度泛函理论(TDDFT)计算,且每个候选立体异构体和构象都需重新计算。本文提出PhysECD,一种物理约束、偶宇称感知的E(3)等变框架,可直接从单个构象的3D结构预测ECD光谱,跳过昂贵的TDDFT计算。该模型不直接回归光谱序列,而是预测生成光谱的物理量:各态激发能及电、磁跃迁偶极矩。这些量决定旋光强度R(两偶极矩的点积,为赝标量,在镜像反射下符号反转),并通过可微的高斯展宽公式得到最终光谱。等变特征的偶宇称结构确保了正确的手性光学对称性:镜像分子的预测光谱完全相反。在CMCDS数据集上,PhysECD达到每分子光谱的皮尔逊相关系数均值0.642、中位数0.822,显著优于先前学习型预测器,同时保持物理可解释性。跨多种骨干结构的实验表明,该框架具有骨架无关性,为实时确定绝对构型开辟了道路。

原文摘要 · Abstract (English)

The electronic circular dichroism (ECD) spectrum is a primary experimental probe for assigning the absolute configuration of chiral molecules, yet interpreting a measured spectrum requires time-dependent density functional theory (TDDFT) calculations that can cost hours per molecule and must be repeated for every candidate stereoisomer and conformation. We present PhysECD, a physics-constrained, parity-aware E(3)-equivariant framework that bypasses computationally expensive TDDFT and predicts ECD spectra directly from the 3D structure of an individual conformer. Instead of regressing the spectrum as an opaque sequence, PhysECD predicts the physical quantities that generate it: per-state excitation energies and electric and magnetic transition dipoles. These quantities determine the rotatory strength R -- the dot product of the two dipoles, a pseudoscalar that reverses sign under mirror reflection -- and yield the final spectrum through a differentiable Gaussian-broadening formula derived from the underlying physics. The parity structure of the equivariant features guarantees the correct chiroptical symmetry: reflecting a molecule exactly negates the predicted spectrum. On the CMCDS dataset, PhysECD attains a per-molecule spectral Pearson correlation of 0.642 (mean) / 0.822 (median), substantially exceeding prior learned predictors while remaining physically interpretable. Experiments across multiple backbones further show that the framework is backbone-agnostic, paving the way for real-time assignment of absolute configuration.

分子性质预测等变神经网络圆二色谱物理约束

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