Accurate identification of the most native-like conformation remains a critical bottleneck in protein complex structure prediction, particularly when multiple globally plausible candidate models are generated by deep learning methods such as AlphaFold3 (AF3). While AF3 provides internal confidence metrics such as ipTM...
Lin-Long Jiang, Yu-Rong Li, Qun Su et al.· Acta Pharmacologica Sinica· 0 citations
Results demonstrate that the proposed method achieves reduced RMSE and bias on synthetic data, as well as higher agreement with conventional NODDI fitting on real data, indicating improved robustness to unseen b-values and a reduced need for retraining.
A. Brigliadori, Leevi Kerkela, Hui Zhang· 0 citations
This work proposes LumiCharge, a novel atomic charge prediction framework that incorporates high-order spherical harmonics convolutions and explicitly models multibody interactions, and demonstrates exceptional extrapolation capability and robustness across molecules of varying sizes, effectively overcoming the limitat...
Qun Su, Hui Zhang, Qiaolin Gou et al.· Journal of Physical Chemistr...· 2 citations
This review provides a comprehensive overview of covalent docking algorithms, systematically categorizing their approaches according to covalent bond formation, which primarily include tethered docking, biased docking, and dynamic covalent docking approaches.
Shi Li, Hongyan Du, Hui Zhang et al.· WIREs Computational Molecula...· 2 citations
This work introduces an advanced equivariant graph attention neural network specifically engineered to model long-range atomic electrostatic interactions with high precision, and improves the model's accuracy, generalization, and robustness in complex scenarios.
Qiaolin Gou, Qun Su, Ji-Ke Wang et al.· Journal of Chemical Informat...· 1 citation
Accurate prediction of metalloprotein-ligand interactions is critical for metalloprotein-targeted drug discovery. Conventional docking tools and existing deep learning (DL) models fail to reliably capture metal-ligand interactions, hampering the discovery of potent metalloprotein inhibitors. Here, we propose MetalloDoc...
Hui Zhang, Xujun Zhang, Qun Su et al.· Journal of the American Chem...· 6 citations
This Account describes a computational and AI-driven ecosystem for structure-based covalent drug discovery and dives into a suite of cutting-edge, AI-driven computational methods, exploring the potential of deep learning in tasks such as molecular docking, covalent binding site prediction, and lead optimization.
Shi Li, Hongyan Du, Xujun Zhang et al.· Accounts of Chemical Researc...· 4 citations
LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed, and enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules.
Qun Su, Kai Zhu, Qiaolin Gou et al.· Nature Communications· 2 citations
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