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
PepPCBench enables a robust evaluation of PFNN-based methods and supports their continued development for peptide-protein structure prediction, and highlights the influence of peptide length, conformational flexibility, and training set similarity on prediction accuracy.
Si-Long Zhai, Huifeng Zhao, Ji-Ke Wang et al.· Journal of Chemical Informat...· 13 citations· ⚡1
CarsiDock-Cov is presented, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.
Chao Shen, Hongyan Du, Xujun Zhang et al.· Acta Pharmaceutica Sinica B· 12 citations
EpiMII is presented, a Graph Neural Network model for MHC-II epitope design, which learns from the structural features of epitopes to predict their sequences, potentially contributing to vaccine development.
Jiayi Yuan, Xiaowei Xu, Ze-Yu Sun et al.· bioRxiv· 1 citation
CovalentLab is introduced, an interactive computational platform that integrates ligand-based and warhead-based strategies into a unified workflow for the rational design of covalent ligands and enables the prediction and ranking of nine classes of covalent-binding residues in proteins according to their reactivity.
Xi Xue, Xiangying Liu, Xue Liu et al.· JACS Au· 4 citations· ⚡1
The Q‐GEM comprises a GNN embedded with the molecular electronic and complete 3D geometrical structural information as well as several well‐designed multiscale SSL tasks, achieving superior absolute molecular conformation prediction and conformational discrimination.
Zhijiang Yang, Liangliang Wang, Tengxin Huang et al.· Advancement of science· 6 citations
This study established a unified model (NRIGN) based on the deep graphic architecture to discriminate agonists and antagonists targeting 26 successful or in-clinical-trial NR targets and achieves an excellent prediction accuracy and is robust enough to be applied in various real-world scenarios.
Kaimo Yang, Dejun Jiang, Qirui Deng et al.· Journal of Chemical Informat...· 2 citations
This study presents ECloudGen, which uses latent diffusion to generate electron clouds from protein pockets and decodes them into molecules, and adopts two-stage training, which expands the chemical space accessible to generative drug design.
This work proposes a novel strategy that incorporates two models: a fragment-based model (Frag-MD) and a character-based model (MD), both integrating a conditional transformer, recurrent neural networks, and reinforcement learning that holds the potential to establish a new paradigm for discovering novel PSs applicable...
Kepeng Chen, Xiaoting Zhang, Ji-Ke Wang et al.· Chemical Science· 4 citations
RAPiDock is presented, an all-atom diffusion model that predicts peptide–protein binding patterns across 92 amino acid types, enabling high-throughput virtual screening for advancing therapeutic peptide design and serve as a powerful tool for high-throughput virtual screening with structural precision.
Abstract Cyclic peptides represent a highly promising class of biopharmaceutical scaffolds. The screening of cyclic peptides against protein targets can be greatly facilitated using computational approaches, especially molecular docking. However, it remains a crucial challenge to accurately predict protein–cyclic pepti...
A unified benchmarking framework is established that enables systematic evaluation of docking methods across diverse tasks and provides critical insights into the strengths and limitations of current docking strategies, thereby informing future developments in protein-protein docking research.
Linlong Jiang, Ke Zhang, Kai Zhu et al.· Journal of Chemical Informat...· 3 citations
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