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 investigate the prevalence of Echinococcus infections in small rodents around human residential areas in Yushu City, Qinghai Province in 2023, so as to provide insights into precision echinococcosis control. Methods One or two quadrats, each measuring 50 m × 50 m, were randomly assigned in Shanglaxiu Township and Longbao Township, Yushu City, Qinghai Province on June 2023, respectively, and 300 plate-type mouse traps, each measuring 12.0 cm × 6.5 cm, were assigned in each quadrat. Small rodents were captured during the period between 10 : 00 and 18 : 00 each day for 4 days. Then, all captured small rodents were identified and dissected, and liver specimens with suspected Echinococcus infections were subjected to pathological examinations. The Echinococcus cytochrome c oxidase 1 (cox1) gene was amplified using PCR assay, and the sequence of the amplified product was aligned to that was recorded in the GenBank to characterize the parasite species. In addition, a phylogenetic tree of Echinococcus was generated based on the cox1 gene sequence using the neighbor-joining method. Results A total of 236 small rodents were captured in Shanglaxiu and Longbao townships, Yushu City, including 65 Qinghai voles and 51 plateau pikas in Shanglaxiu Township, and 62 Qinghai voles and 58 plateau pikas in Longbao Township, and there was no significant difference in the constituent ratio of small rodents between the two townships (χ² = 0.294, P > 0.05). Seven plateau pikas and 12 Qinghai voles were suspected to be infected with Echinococcus by dissection, and pathological examinations showed unclear structure of hepatic lobules and disordered hepatocyte arrangement in livers of small rodents suspected of Echinococcus infections. PCR assay identified E. shiquicus DNA in 7 Qinghai voles, which were all captured from Shanglaxiu Township. Phylogenetic analysis showed that the cox1 gene sequence of Echinococcus in small rodents was highly homologous to the E. shiquicus cox1 gene sequence reported previously. Conclusion Plateau pika and Qinghai vole were predominant small rodents around human residential areas in Yushu City, Qinghai Province in 2023, and E. shiquicus infection was detected in Qinghai voles.

Objective:To establish a decision tree model of pediatric complicated appendicitis (CA) based on Pediatric Appendicitis Score (PAS) combined with inflammatory indicators, and to evaluate its clinical application efficacy in pediatrics.Methods:The clinical data of 544 children diagnosed with appendicitis in Children′s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021 was retrospectively analyzed. According to postoperative pathology, the children were divided into uncomplicated appendicitis group and CA group. The independent risk factors of CA were screened by univariate and multivariate logistic regression analysis, and these parameters were included to establish the decision tree model. The accuracy of the decision tree model was verified by receiver operating characteristic (ROC) curve.Results:Binary logistic regression analysis indicated that the PAS, C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR) were identified as independent risk factors for complicated appendicitis in children (all P<0.05). PAS, CRP and NLR were included as covariables to construct the decision tree model and binary logistic regression model for predicting CA. The decision tree demonstrated an overall accuracy of 79.2% with a sensitivity of 86.7% and specificity of 71.9%, and achieved an area under curve (AUC) of 0.821(95% CI: 0.786-0.857). The binary logistic regression model had a sensitivity of 79.6% and specificity of 69.1%, with an overall accuracy of 75.1% and achieved an AUC of 0.808(95% CI: 0.770-0.845). Conclusions:The decision tree model based on PAS score combined with CRP, NLR is a simple, intuitive and effective tool , which can provide pediatric emergency physicians a reliable basis for diagnosis of pediatric CA.

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).

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.

Chaperone-mediated autophagy (CMA) is a lysosome-dependent selective degradation pathway implicated in the pathogenesis of cancer and neurodegenerative diseases. However, the mechanisms that regulate CMA are not fully understood. Here, using unbiased drug screening approaches, we discover Metformin, a drug that is commonly the first medication prescribed for type 2 diabetes, can induce CMA. We delineate the mechanism of CMA induction by Metformin to be via activation of TAK1-IKKα/β signaling that leads to phosphorylation of Ser85 of the key mediator of CMA, Hsc70, and its activation. Notably, we find that amyloid-beta precursor protein (APP) is a CMA substrate and that it binds to Hsc70 in an IKKα/β-dependent manner. The inhibition of CMA-mediated degradation of APP enhances its cytotoxicity. Importantly, we find that in the APP/PS1 mouse model of Alzheimer's disease (AD), activation of CMA by Hsc70 overexpression or Metformin potently reduces the accumulated brain Aβ plaque levels and reverses the molecular and behavioral AD phenotypes. Our study elucidates a novel mechanism of CMA regulation via Metformin-TAK1-IKKα/β-Hsc70 signaling and suggests Metformin as a new activator of CMA for diseases, such as AD, where such therapeutic intervention could be beneficial.

Protein neddylation is catalyzed by a three-enzyme cascade, namely an E1 NEDD8-activating enzyme (NAE), one of two E2 NEDD8 conjugation enzymes and one of several E3 NEDD8 ligases. The physiological substrates of neddylation are the family members of cullin, the scaffold component of cullin RING ligases (CRLs). Currently, a potent E1 inhibitor, MLN4924, also known as pevonedistat, is in several clinical trials for anti-cancer therapy. Here we report the discovery, through virtual screening and structural modifications, of a small molecule compound HA-1141 that directly binds to NAE in both

Objective:This paper briefly reviewed the literature related to skin flaps published in Chinese Journal of Plastic Surgery ( CJPS) and Plastic and Reconstructive Surgery ( PRS) in recent five years (from January 2016 to December 2020) to compare the similarities and differences between these two journals and to direct future research. Methods:In May of 2021, literature with "flap" as the keyword in the titles and abstracts published in CJPS and PRS in recent five years were searched. Related literature published by CJPS were searched by Wanfang Data and CNKI database, and that of PRS were searched by PubMed and Scopus database. After removing the repetitive literature, the titles and abstracts were read to exclude the non-flap studies and non-original articles. By reading the full text and using bibliometrics, the total number of papers published, the number of papers on flap research, the level of evidence-based medicine evidence, the nationality and organization distribution of the authors, the type of flaps, the application of flaps, and new technologies were comprehensively analyzed. Results:The total number of papers published by CJPS in recent five years was 1 116, and 244 were included in this study. The total number of articles published in PRS in the same period was 4 562, and 268 were included in this study. Most of the articles published in PRS are from American authors. The number of articles published by Chinese authors is in the second place. In the past five years, authors from the mainland of China published 21 papers in PRS. Most of the articles published by CJPS are about the pedicle flap, while PRS is about the free flap. CJPS published more articles about the traditional flaps than perforator flaps, and PRS did the opposite. CJPS published articles mainly on the local flap, anterolateral thigh flap, and peroneal artery perforator flap, while PRS focused on the inferior epigastric artery perforator fibula bone or osteocutaneous flap, and anterolateral thigh flap. The indication of flap surgery reported by CJPS is the reconstruction of various defects, while the flaps reported by PRS are mainly used in breast reconstruction and other fields. In addition, computer-aided imaging, indocyanine green angiography, propeller flap, multilobed flap, and other new technologies and concepts have been widely reported in the literature related to skin flaps published in CPJS and PRS. Conclusions:In the recent five years, the flap research in China has been at the leading international level and has certain competitiveness. However, the study in China is limited to reporting clinical experience, and the level of evidence-based medicine is relatively low, so there is still a certain gap with the international frontier research.