Physicochemically Informed Axial Chirality Descriptors Enable Accurate Prediction of Atropisomeric Stability.

Journal: Angewandte Chemie (International ed. in English)
Published Date: Dec 10, 2025

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 mol-1 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.

Authors

  • Haisong Xu
    Department of Plastic and Reconstructive Surgery, Shanghai Tissue Engineering Key Laboratory, Shanghai Research Institute of Plastic and Reconstructive Surgery, Shanghai 9th People's Hospital, Rapid Prototyping Center of Shanghai University China.
  • Tingting Du
    Department of Radiation Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
  • Jiali Lin
    Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
  • Feiying You
    School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong 518172, China.
  • Yingbo Shao
    Haihe Laboratory of Sustainable Chemical Transformations, Tianjin, 300192, China.
  • Qi Yang
    Department of Radiology, The First Hospital of Jilin University, No.1, Xinmin Street, Changchun 130021, China (Y.W., M.L., Z.M., J.W., K.H., Q.Y., L.Z., L.M., H.Z.).
  • Xin Li
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.

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