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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.
Advances in elucidating the molecular mechanisms underlying AR conformational regulation are summarized and progress in the structure-based design and development of novel AR antagonists are highlighted, highlighting the power of computation-driven approaches in drug discovery.
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.
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...
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...
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.
These results suggest that it will be much time-saving to utilize RAMD with high random force for interaction pathway exploration for both the pathway obvious and unobvious systems if the protein keeps stable in the simulation if the protein keeps stable in the simulation.
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...
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.
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.
The DRHIN platform provides a code-free portal supporting three key predictive tasks: discovering drug-disease associations, repurposing existing drugs for new indications, and identifying potential therapies for specific diseases, making analyses accessible and reproducible.
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.
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