The interest in covalent drugs has resurged in recent decades, spurring the development of numerous specialized computational docking tools to facilitate covalent ligand design and screening. Herein, we present CarsiDock-Cov, a new paradigm distinguishing itself as the first deep learning (DL)-guided approach for covalent docking. CarsiDock-Cov retains the core components of its non-covalent predecessor, leveraging a DL model pretrained on millions of docking complexes to predict protein-ligand distance matrices, along with a dedicated-designed geometric optimization procedure to convert these distances into refined binding poses. Additionally, it incorporates several key enhancements specifically tailored to optimize the protocol for covalent docking applications. Our approach has been extensively validated on multiple public datasets regarding the docking and screening of covalent ligands, and the results indicate that our approach not only achieves comparably improved applicability compared to its non-covalent predecessor, but also exhibits competitive performance against various state-of-the-art covalent docking tools. Collectively, our approach represents a significant advance in covalent docking methodology, offering an automated and efficient solution that shows considerable promise for accelerating covalent drug discovery and design.

Objective: To investigate the proteomic characteristics of overweight/obesity and related abnormal glucose and lipid metabolism caused by phlegm-dampness retention to identify related biomarkers. Methods: Seventy-one subjects were enrolled in the study. We assessed blood glucose, blood lipids, body mass index (BMI), and phlegm-dampness pattern, which was confirmed by a traditional Chinese med-icine clinician. Of the participants, we included healthy participants with normal weight (NW, n =23), overweight/obese participants with normal metabolism (ONM, n = 19), overweight/obese participants with pre-diabetes (OPD, n = 12), and overweight/obese participants with marginally-elevated blood lipids (OML, n = 17). Among them, the ONM, OPD, and OML groups were diagnosed with phlegm-dampness pattern. The data-independent acquisition (DIA) method was first used to analyze the plasma protein expression of each group, and the relevant differential proteins of each group were screened. The co-expressed proteins were evaluated by Venn analysis. The pathway analyses of the differential proteins were analyzed using Ingenuity Pathway Analysis (IPA) software. Parallel reaction monitoring (PRM) was used to verify the differential and common proteins in each group. Results: After comparing ONM, OPD, and OML groups with NW group, we identified the differentially expressed proteins (DEPs). Next, we determined the DEPs among OPD, OML, and ONM groups. Using Venn analysis of the DEPs in each group, 24 co-expressed proteins were screened. Two co-expressed proteins were verified by PRM. IPA analysis showed that pathways including LXR/RXR activation, acute phase response signaling, and FXR/RXR activation were common to all three groups of phlegm-damp overweight/obesity participants. However, the activation or inhibition of these pathways was different among the three groups. Conclusion: Participants with overweight/obesity have similar proteomic characteristics, though each type shows specific proteomic characteristics. Two co-expressed proteins, VTN and ORM1, are potential biomarkers for glucose and lipid metabolism diseases with overweight/obesity caused by phlegm-dampness retention.