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
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
Macrocycle-DB is presented, the most extensive online database dedicated to macrocycles, featuring 45 525 compounds, including 76 approved drugs and 105 clinical candidates that target 2533 proteins.
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
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
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
