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
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
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.
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
The results indicate that this fully automated, open-source system holds potential value for improving the efficiency and sustainability of molecular synthesis, and the integration of organic and enzymatic synthesis enhances molecule construction efficiency.
Token-Mol is presented, a token-only 3D drug design model that encodes both 2D and 3D structural information, along with molecular properties, into discrete tokens, which introduces a Gaussian cross-entropy loss function tailored for regression tasks, enabling superior performance across multiple downstream application...
Ji-Ke Wang, Rui Qin, Mingyang Wang et al.· Nature Communications· 30 citations· ⚡1
AgentFold is presented, a multi-agent framework that formulates folding-model development as a closed-loop search over executable code variants and improves the best lDDT by 7.5% over independent Codex proposals and outperforms a random-search control.
Ming-Quan Liu, Jiangyue Chen, Han-Qun Cao et al.· 0 citations
A novel committor learning framework grounded in the AlphaFold 3 paradigm is proposed that elucidates how ligand substituents regulate the ratio between distinct binding pathways, offering new perspectives for structure-based drug design.
Jintu Zhang, Zichang Jin, Huifeng Zhao et al.· 0 citations
The Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein-ligand distances and reconstructs binding poses; and RTMScor...
