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.

P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates. To overcome this challenge, we compiled an extensive dataset from public databases and literature, consisting of 5,943 P-gp inhibitors and 4,018 substrates, notable for their high quantity, quality, and structural uniqueness. In addition, we curated two external test sets to validate the model's generalization capability. Subsequently, we developed a multimodal graph contrastive learning (GCL) model for the prediction of P-gp inhibitors and substrates (MC-PGP). This framework integrates three types of features from Simplified Molecular Input Line Entry System (SMILES) sequences, molecular fingerprints, and molecular graphs using an attention-based fusion strategy to generate a unified molecular representation. Furthermore, we employed a GCL approach to enhance structural representations by aligning local and global structures. Extensive experimental results highlight the superior performance of MC-PGP, which achieves improvements in the area under the curve of receiver operating characteristic (AUC-ROC) of 9.82% and 10.62% on the external P-gp inhibitor and external P-gp substrate datasets, respectively, compared with 12 state-of-the-art methods. Furthermore, the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights, demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions. These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.

Objective:To explore and compare the clinical application value of 8-channel head phased-array coil, an 8-channel temporomandibular joint (TMJ)-specific surface coil, and a single-channel surface coil in TMJ MRI examinations.Methods:A total of 600 temporomandibular disorders (TMD) patients (1 200 joints) who underwent TMJ MRI examination in the First People′s Hospital of Jinzhong from June 2020 to January 2025 were retrospectively screened. Based on inclusion/exclusion criteria, 120 TMD patients (240 joints) with closed-mouth oblique sagittal proton density weighted imaging (OSag PDWI), coronal T2 fat-suppression weighted imaging (OCor fs T2WI) and open-mouth oblique sagittal proton density weighted imaging (OSag PDWI) were included. Patients were divided into groups A, B, and C, with 40 cases in each group. Group A (31female, 9male, median age 24 years old), underwent 8-channel head phased-array coil imaging. Group B (29 female, 11male, median age 23.5 years old) underwent TMJ imaging with an 8-channel surface coil. Group C (29 female, 11male, median age 22.5 years old) underwent single-channel surface coil imaging. There were no significant differences in age, gender, type or disease types among groups ( P>0.05). Six healthy volunteers without TMD (4 female, 2 male, range 19 to 45 years old) underwent imaging with all three coils as self-control. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image quality were compared for five regions of interest (ROI) in both patients and volunteers. Results:Under the same sequence and the same parameters, SNR and CNR in group B were higher than those in group A, and SNR and CNR in group C were also higher than those in group A, the differences were statistically significant ( P<0.05). However, there were significant differences in SNR and CNR between group B and group C in the closed and open positions of ROI1, the open positions of ROI3 and the open positions of ROI5 ( P<0.05), and there were no significant differences in other positions ( P>0.05). Group B had the best image quality, followed by group C and group A had the worst image quality. There were significant differences in the visualization of OSag PDWI in the closed mouth position, OCor T2WI in the coronal position, and OSag PDWI in the open mouth position, such as condyle, anterior attachment, joint disc, double lamina area, joint cavity and lateral pterygoid muscle ( P<0.05). There were significant differences between group B and group C in showing the joint cavity in the closed mouth position and showing the structure of the bilaminar area in the open mouth position ( P<0.05). There was no significant difference in other regions of interest ( P>0.05). The subjective scores of condyle, anterior attachment, articular disc, bilaminar area, articular cavity, lateral pterygos muscle and other structures were medium to high in group A, high in group B, and high or high in group C by two radiologists independently. In the five rois, the 8-channel TMJ surface coil showed more details, especially in the articular disc, condyle and lateral pterygoid muscle regions, and had more advantages in both volunteers and patients. Conclusions:The 8-channel TMJ-specific surface coil provides significantly clearer visualization of critical anatomical details within the ROIs, demonstrating the highest clinical application value and is recommended as the preferred choice.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

P-glycoprotein(P-gp)is a transmembrane protein widely involved in the absorption,distribution,metabolism,excretion,and toxicity(ADMET)of drugs within the human body.Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment.However,existing models rely on limited molecular information,leading to suboptimal model performance for predicting P-gp inhibitors and substrates.To overcome this challenge,we compiled an extensive dataset from public databases and literature,consisting of 5,943 P-gp inhibitors and 4,018 substrates,notable for their high quantity,quality,and structural uniqueness.In addition,we curated two external test sets to validate the model's generalization capability.Subsequently,we developed a multimodal graph contrastive learning(GCL)model for the prediction of P-gp inhibitors and substrates(MC-PGP).This framework integrates three types of features from Simplified Molecular Input Line Entry System(SMILES)sequences,molecular fingerprints,and molecular graphs using an attention-based fusion strategy to generate a unified mo-lecular representation.Furthermore,we employed a GCL approach to enhance structural representations by aligning local and global structures.Extensive experimental results highlight the superior perfor-mance of MC-PGP,which achieves improvements in the area under the curve of receiver operating characteristic(AUC-ROC)of 9.82％and 10.62％on the external P-gp inhibitor and external P-gp substrate datasets,respectively,compared with 12 state-of-the-art methods.Furthermore,the interpretability analysis of all three molecular feature types offers comprehensive and complementary insights,demonstrating that MC-PGP effectively identifies key functional groups involved in P-gp interactions.These chemically intuitive insights provide valuable guidance for the design and optimization of drug candidates.

Objective:To explore and compare the clinical application value of 8-channel head phased-array coil, an 8-channel temporomandibular joint (TMJ)-specific surface coil, and a single-channel surface coil in TMJ MRI examinations.Methods:A total of 600 temporomandibular disorders (TMD) patients (1 200 joints) who underwent TMJ MRI examination in the First People′s Hospital of Jinzhong from June 2020 to January 2025 were retrospectively screened. Based on inclusion/exclusion criteria, 120 TMD patients (240 joints) with closed-mouth oblique sagittal proton density weighted imaging (OSag PDWI), coronal T2 fat-suppression weighted imaging (OCor fs T2WI) and open-mouth oblique sagittal proton density weighted imaging (OSag PDWI) were included. Patients were divided into groups A, B, and C, with 40 cases in each group. Group A (31female, 9male, median age 24 years old), underwent 8-channel head phased-array coil imaging. Group B (29 female, 11male, median age 23.5 years old) underwent TMJ imaging with an 8-channel surface coil. Group C (29 female, 11male, median age 22.5 years old) underwent single-channel surface coil imaging. There were no significant differences in age, gender, type or disease types among groups ( P>0.05). Six healthy volunteers without TMD (4 female, 2 male, range 19 to 45 years old) underwent imaging with all three coils as self-control. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image quality were compared for five regions of interest (ROI) in both patients and volunteers. Results:Under the same sequence and the same parameters, SNR and CNR in group B were higher than those in group A, and SNR and CNR in group C were also higher than those in group A, the differences were statistically significant ( P<0.05). However, there were significant differences in SNR and CNR between group B and group C in the closed and open positions of ROI1, the open positions of ROI3 and the open positions of ROI5 ( P<0.05), and there were no significant differences in other positions ( P>0.05). Group B had the best image quality, followed by group C and group A had the worst image quality. There were significant differences in the visualization of OSag PDWI in the closed mouth position, OCor T2WI in the coronal position, and OSag PDWI in the open mouth position, such as condyle, anterior attachment, joint disc, double lamina area, joint cavity and lateral pterygoid muscle ( P<0.05). There were significant differences between group B and group C in showing the joint cavity in the closed mouth position and showing the structure of the bilaminar area in the open mouth position ( P<0.05). There was no significant difference in other regions of interest ( P>0.05). The subjective scores of condyle, anterior attachment, articular disc, bilaminar area, articular cavity, lateral pterygos muscle and other structures were medium to high in group A, high in group B, and high or high in group C by two radiologists independently. In the five rois, the 8-channel TMJ surface coil showed more details, especially in the articular disc, condyle and lateral pterygoid muscle regions, and had more advantages in both volunteers and patients. Conclusions:The 8-channel TMJ-specific surface coil provides significantly clearer visualization of critical anatomical details within the ROIs, demonstrating the highest clinical application value and is recommended as the preferred choice.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

The new generation of artificial intelligence technology,represented by deep learning,has emerged as a crucial driving force in the advancement of new drug research and development.This article creatively proposes a workflow named"Molecular Factory"for the design and optimization of drug molecules based on artificial intelligence technology.This workflow integrates intelligent molecular generation models,high-performance molecular docking algorithms,and accurate protein-ligand binding affinity prediction methods.It has been integrated as a core module into DrugFlow,a one-stop drug design software platform,providing a comprehensive set of mature solutions for the discovery and optimization of lead compounds.Utilizing the"Molecular Factory"module,we conducted the research of second-generation inhibitors against Menin that can combat drug resistance.Through the integration of computational and experimental approaches,we rapidly identified multiple promising compounds.Among them,compound RG-10 exhibited the IC50 values of 9.681 nmol/L,233.2 nmol/L,and 40.09 nmol/L against the wild-type Menin,M327I mutant,and T349M mutant,respectively.Compared to the positive reference molecule SNDX-5613,which has entered Phase Ⅱ clinical trials,RG-10 demonstrated significantly enhanced inhibitory activity against the M327I and T349M mutants.These findings fully demonstrate the unique advantages of the"Molecular Factory"technology in practical drug design and development scenarios.It can rapidly and efficiently generate high-quality active molecules targeting specific protein structures,holding significant value and profound implications for advancing new drug discovery.

Acid-base dissociation constant (pK_a) is a key physicochemical parameter in chemical science, especially in organic synthesis and drug discovery. Current methodologies for pK_a prediction still suffer from limited applicability domain and lack of chemical insight. Here we present MF-SuP-pK_a (multi-fidelity modeling with subgraph pooling for pK_a prediction), a novel pK_a prediction model that utilizes subgraph pooling, multi-fidelity learning and data augmentation. In our model, a knowledge-aware subgraph pooling strategy was designed to capture the local and global environments around the ionization sites for micro-pK_a prediction. To overcome the scarcity of accurate pK_a data, low-fidelity data (computational pK_a) was used to fit the high-fidelity data (experimental pK_a) through transfer learning. The final MF-SuP-pK_a model was constructed by pre-training on the augmented ChEMBL data set and fine-tuning on the DataWarrior data set. Extensive evaluation on the DataWarrior data set and three benchmark data sets shows that MF-SuP-pK_a achieves superior performances to the state-of-the-art pK_a prediction models while requires much less high-fidelity training data. Compared with Attentive FP, MF-SuP-pK_a achieves 23.83% and 20.12% improvement in terms of mean absolute error (MAE) on the acidic and basic sets, respectively.

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural networks (GNN) models on the internal test set. Furthermore, it also exhibited much better performance on two external test sets, suggesting that the AMGU model has enhanced generalizability due to its great transfer learning capacity. Then, a naïve model-agnostic interpretable method for GNN called edges masking was devised to explain the underlying predictive mechanisms, and the consistency of the interpretability results for 5 typical epidermal growth factor receptor (EGFR) inhibitors with their structure‒activity relationships could be observed. Finally, a free online web server called KIP was developed to predict the kinome-wide polypharmacology effects of small molecules (http://cadd.zju.edu.cn/kip).