NavDB is a specialized and open-access database focusing on VGSC modulators and targets that integrates 8023 curated data records covering 5168 compounds, including small molecules, toxins, drugs, and peptides, along with comprehensive annotations on biological activity, druggability, and structural feature.
Gaoang Wang, Jiahui Yu, Haiyi Chen et al.· Journal of Chemical Informat...· 0 citations
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
An explainable intelligence computational framework, Symbolic Trajectory-Embedded Dark Causal Interaction Inference (STE-DC2I), which combines symbolic trajectory embedding with historical prediction mechanisms to model nonmonotonic oscillatory dependencies between genes in CRC subtypes offers interpretable insights an...
Meng Huang, Huijin Hu, Ming Li et al.· Journal of Chemical Informat...· 0 citations
D8, which contained 2-oxo-tetrahydroquinoline by carbonyl migration form B53, manifests an excellent SGRM with remarkable transrepression potency and optimized binding mode within the GR LBD, underscoring its therapeutic potential and validating the design strategy.
Xiaodong Bao, Yuxin Zhou, Zhaoxu Yang et al.· Journal of Medicinal Chemist...· 1 citation
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This study leverages an integrated computational strategy combining molecular dynamics simulation, end-point binding free-energy calculation, and enhanced sampling technologies to elucidate the dynamic characteristics of RAS-ligand-CYPA interactions and uncover the dynamic process of stabilizer-mediated KRAS-CYPA stabi...
Kexin Xu, Mingyun Shen, Zhe Wang et al.· Journal of Chemical Informat...· 0 citations
This work aims to discuss the features and the generative performance of different types of molecular generative models for the PROTAC design task and help researchers to better apply these models in practical cases.
Jieyu Jin, Tingjun Hou, Huanxiang Liu et al.· Journal of Chemical Informat...· 0 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
MCGLPPI, a novel geometric representation learning framework that combines graph neural networks (GNNs) with the MARTINI molecular coarse-grained (CG) model to predict overall PPI properties accurately and efficiently, offers an effective and efficient solution for PPI overall property predictions.
Yang Yue, Shu Li, Yihua Cheng et al.· bioRxiv· 15 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.
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
The authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks, which signifies a substantial leap forward in mRNA research and therapeutic development.
Ying Xiong, Aowen Wang, Yu Kang et al.· Nature Communications· 23 citations· ⚡1
OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.
