近日，南开大学李鑫团队报道了基于物理化学信息的轴向手性描述符可准确预测阻旋异构体的稳定性。该研究于2025年12月10日发表在《德国应用化学》杂志上。

阻旋异构体在不对称合成、药物发现及功能材料开发中起着至关重要的作用。然而，由于难以预测其构型稳定性（该性质取决于旋转能垒ΔG^‡），对阻旋异构体的理性设计充满挑战。

研究组提出名为ACSD-GAT的深度学习框架以解决该问题：构建了包含1015个实验测定旋转能垒的全新基准数据集，并开发了具有物理化学意义的轴向手性结构描述符（ACSD），该描述符可定量表征旋转过程中的静态与动态空间排斥效应。通过将ACSD与图注意力网络（GAT）相结合，该模型能精准预测旋转能垒，在测试数据集上实现了R²=0.91、均方根误差为2.02千卡/摩尔的优异性能。通过对复杂药物分子、分子开关及新合成阻旋异构体的严格验证，进一步证明了该模型的鲁棒性与实际应用潜力。

附：英文原文

Title: Physicochemically Informed Axial Chirality Descriptors Enable Accurate Prediction of Atropisomeric Stability

Author: Haisong Xu, Tingting Du, Jiali Lin, Feiying You, Yingbo Shao, Qi Yang, Xin Li

Issue&Volume: 2025-12-10

Abstract: Atropisomers play a vital role in asymmetric synthesis, drug discovery, and the development of functional materials. However, the rational design of atropisomers is challenging due to the difficulty in predicting their configurational stability, which depends on the rotational barrier (ΔG). Here, we introduce ACSD-GAT, a deep learning framework that addresses this issue. Our approach comprises a newly curated benchmark dataset of 1015 experimentally measured rotational barrier, along with a physicochemically informed axial chirality structure descriptor (ACSD) that explicitly quantifies both static and dynamic steric repulsion during rotation. By integrating the ACSD with a graph attention network (GAT), our model accurately predicts the rotational barrier, achieving an R2 of 0.91 and a RMSE of 2.02 kcal mol1 on test datasets. The robustness and real-world applicability of the model are also demonstrated through rigorous validation with complex pharmaceuticals, molecular switches, and newly synthesized atropisomers.

DOI: 10.1002/anie.202521349

Source: https://onlinelibrary.wiley.com/doi/10.1002/anie.202521349