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Integrated causal inference modeling uncovers novel causal factors and potential therapeutic targets of Qingjin Yiqi granules for chronic fatigue syndrome

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Acupuncture and Herbal Medicine 2024年第1期4卷 122-133页

作者： Junrong Li Xiaobing Zhai Jixing Liu Chi Kin Lam Weiyu Meng Yuefei Wang Shu Li Yapeng Wang Kefeng Li Faculty of Applied Sciences Macao Polytechnic UniversityMacao SARChina Center for artificial intelligence driven drug discovery Faculty of Applied SciencesMacao Polytechnic UniversityMacao SARChina National Key Laboratory of Chinese Medicine Modernization Tianjin Key Laboratory of TCM Chemistry and AnalysisTianjin University of Traditional Chinese MedicineTianjinChina Haihe Laboratory of Modern Chinese Medicine TianjinChina

Objective:Chronic fatigue syndrome(CFS)is a prevalent symptom of post-coronavirus disease 2019(COVID-19)and is associated with unclear disease *** herbal medicine Qingjin Yiqi granules(QJYQ)constitute a clinically approved formula for treating post-COVID-19;however,its potential as a drug target for treating CFS remains largely *** study aimed to identify novel causal factors for CFS and elucidate the potential targets and pharmacological mechanisms of action of QJYQ in treating ***:This prospective cohort analysis included 4,212 adults aged≥65 years who were followed up for 7 years with 435 incident CFS *** modeling and multivariate logistic regression analysis were performed to identify the potential causal determinants of CFS.A proteome-wide,two-sample Mendelian randomization(MR)analysis was employed to explore the proteins associated with the identified causal factors of CFS,which may serve as potential drug ***,we performed a virtual screening analysis to assess the binding affinity between the bioactive compounds in QJYQ and CFS-associated ***:Among 4,212 participants(47.5%men)with a median age of 69 years(interquartile range:69–70 years)enrolled in 2004,435 developed CFS by *** graph analysis with multivariate logistic regression identified frequent cough(odds ratio:1.74,95%confidence interval[CI]:1.15–2.63)and insomnia(odds ratio:2.59,95%CI:1.77–3.79)as novel causal factors of ***-wide MR analysis revealed that the upregulation of endothelial cell-selective adhesion molecule(ESAM)was causally linked to both chronic cough(odds ratio:1.019,95%CI:1.012–1.026,P=2.75 e^(−05))and insomnia(odds ratio:1.015,95%CI:1.008–1.022,P=4.40 e^(−08))in *** major bioactive compounds of QJYQ,ginsenoside Rb2(docking score:−6.03)and RG4(docking score:−6.15),bound to ESAM with high affinity based on virtual ***:Our integrated analytical framework combining epidemiological,genet

关键词： Causal factors Causal graph analysis Chronic fatigue syndrome drug targets Mendelian randomization Qingjin Yiqi

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Integrating sequence and graph information for enhanced drug-target affinity prediction

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Science China(Information Sciences) 2024年第2期67卷 325-326页

作者： Haohuai HE Guanxing CHEN Calvin Yu-Chian CHEN artificial intelligence Medical Research Center School of Intelligent Systems EngineeringShenzhen Campus of Sun Yat-sen University

drug discovery is a pivotal discipline involving the identification and development of innovative pharmaceuticals designed to combat a wide array of illnesses. It plays a crucial role in advancing global health outcomes and improving the standard of living for individuals worldwide. However,drug discovery often relies on laborious in vitro experiments,

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A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites

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Research 2024年第2期2023卷 703-718页

作者： Minjie Mou Ziqi Pan Zhimeng Zhou Lingyan Zheng Hanyu Zhang Shuiyang Shi Fengcheng Li Xiuna Sun Feng Zhu College of Pharmaceutical Sciences The Second Affliated HospitalZhejiang University School of MedicineNational Key Laboratory of Advanced Drug Delivery and Release SystemsZhejiang UniversityHangzhou 310058China Innovation Institute for artificial intelligence in Medicine of Zhejiang University Alibaba-Zhejiang University Joint Research Center of Future Digital HealthcareHangzhou 330110China

The identification of protein-protein interaction (PPI) sites is essential in the research of protein function and the discovery of new drugs. So far, a variety of computational tools based on machine learning have been developed to accelerate the identification of PPI sites. However, existing methods suffer from the low predictive accuracy or the limited scope of application. Specifically, some methods learned only global or local sequential features, leading to low predictive accuracy, while others achieved improved performance by extracting residue interactions from structures but were limited in their application scope for the serious dependence on precise structure information. There is an urgent need to develop a method that integrates comprehensive information to realize proteome-wide accurate profiling of PPI sites. Herein, a novel ensemble framework for PPI sites prediction, EnsemPPIS, was therefore proposed based on transformer and gated convolutional networks. EnsemPPIS can effectively capture not only global and local patterns but also residue interactions. Specifically, EnsemPPIS was unique in (a) extracting residue interactions from protein sequences with transformer and (b) further integrating global and local sequential features with the ensemble learning strategy. Compared with various existing methods, EnsemPPIS exhibited either superior performance or broader applicability on multiple PPI sites prediction tasks. Moreover, pattern analysis based on the interpretability of EnsemPPIS demonstrated that EnsemPPIS was fully capable of learning residue interactions within the local structure of PPI sites using only sequence information. The web server of EnsemPPIS is freely available at http://***/ensemppis.

关键词： residue extracting ensemble

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AI enhances drug discovery and development

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National Science Review 2024年第3期11卷 52-54页

作者： Fang Bai Shiliang Li Honglin Li Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology ShanghaiTech University Lingang Laboratory Innovation Center for AI and drug discovery East China Normal University

The advent of the third wave of artificial intelligence(AI) heralds a new era in drug discovery. AI-powered techniques can be characteristic of identifying features that are difficult for humans to interpret from big ...

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Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

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Research 2024年第2期2023卷 773-794页

作者： Xiaorui Wang Chang-Yu Hsieh Xiaodan Yin Jike Wang Yuquan Li Yafeng Deng Dejun Jiang Zhenxing Wu Hongyan Du Hongming Chen Yun Li Huanxiang Liu Yuwei Wang Pei Luo Tingjun Hou Xiaojun Yao Dr.Neher's Biophysics Laboratory for lnnovative drug discovery State Key Laboratory of Quality Research in Chinese MedicineMacao Institute for Applied Research in Medicine and HealthMacao University of Science and TechnologyMacao999078China innovation Institute for artificial intelligence in Medicine of Zhejiang University College of Pharmaceutical SciencesZhejiang UniversityHangzhou310058China Faculty of Applied Sciences Macao Polytechnic UniversityMacao999078China College of Chemistry and Chemical Engineering Lanzhou UniversityLanzhou730000China Carbon Silicon AI Technology Co. LtdHangzhouZhejiang 310018China Center of Chemistry and Chemical Biology Guangzhou Regenerative Medicine and Health Guangdong LaboratoryGuangzhou 510530China College of Pharmacy Shaanxi University of Chinese MedicineXianyangShaanxi712044China

Effective synthesis planning powered by deep learning(DL)can significantly accelerate the discovery of new drugs and ***,most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions(RCs)for their reaction ***,the prediction of RCs with a DL framework is hindered by several factors,including:(a)lack of a standardized dataset for benchmarking,(b)lack of a general prediction model with powerful representation,and(c)lack of *** address these issues,we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named *** careful design of the model architecture,pretraining method,and training strategy,Parrot improved the overall top-3 prediction accuracy on catalysis,solvents,and other reagents by as much as 13.44%,compared to the best previous model on a newly curated ***,the mean absolute error of the predicted temperatures was reduced by about 4℃.Furthermore,Parrot manifests strong generalization capacity with superior cross-chemical-space prediction *** analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of *** proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable,generalizable,and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

关键词： versatile absolute generalization

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In silico off-target profiling for enhanced drug safety assessment

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Acta Pharmaceutica Sinica B 2024年第7期14卷 2927-2941页

作者： Jin Liu Yike Gui Jingxin Rao Jingjing Sun Gang Wang Qun Ren Ning Qu Buying Niu Zhiyi Chene Xia Sheng Yitian Wang Mingyue Zheng Xutong Li College of Pharmaceutical Sciences Zhejiang UniversityHangzhou 310058China drug discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia Medica Chinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China Nanjing University of Chinese Medicine Nanjing 210023China School of Pharmaceutical Science and Technology Hangzhou Institute for Advanced StudyHangzhou 330106China

Ensuring drug safety in the early stages of drug development is crucial to avoid costly failures in subsequent ***,the economic burden associated with detecting drug off-targets and potential side effects through in vitro safety screening and animal testing is *** off-target interactions,along with the adverse drug reactions they induce,are significant factors affecting drug *** assess the liability of candidate drugs,we developed an artificial intelligence model for the precise prediction of compound off-target interactions,leveraging multi-task graph neural *** outcomes of off-target predictions can serve as representations for compounds,enabling the differentiation of drugs under various ATC codes and the classification of compound ***,the predicted off-target profiles are employed in adverse drug reaction(ADR)enrichment analysis,facilitating the inference of potential ADRs for a *** the withdrawn drug Pergolide as an example,we elucidate the mechanisms underlying ADRs at the target level,contributing to the exploration of the potential clinical relevance of newly predicted off-target ***,our work facilitates the early assessment of compound safety/toxicity based on off-target identification,deduces potential ADRs of drugs,and ultimately promotes the secure development of drugs.

关键词： drug safety Off-target prediction Adverse drug reactions Toxicity Molecular representation artificial intelligence

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αExtractor: a system for automatic extraction of chemical information from biomedical literature

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Science China(Life Sciences) 2024年第3期67卷 618-621页

作者： Jiacheng Xiong Xiaohong Liu Zhaojun Li Hongzhong Xiao Guangchao Wang Zhenjiang Niu Chaoyuan Fei Feisheng Zhong Gang Wang Wei Zhang Zunyun Fu Zhiguo Liu Kaixian Chen Hualiang Jiang Mingyue Zheng drug discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215125China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China

Dear Editor,Great progress has been made using artificial intelligence(AI) techniques in learning knowledge from biomedical databases in recent years, revolutionizing the study of many fields, such as protein structure prediction and protein design(Madani et al., 2023). However, there is massive biomedical knowledge not curated in the form of structured data but hidden in primary scientific literature.

关键词： has such knowledge

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Intelligent design and construction of novel APN-based theranostic probe driven by advanced computational methods

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Chinese Chemical Letters 2024年第2期35卷 352-357页

作者： Yingli Zhu Jie Qian Kunqian Yu Jing Hou Yeshuo Ma Fei Chen Jie Dong Wenbin Zeng Xiangya School of Pharmaceutical Sciences Central South UniversityChangsha 410083China National Engineering Research Center of Rice and Byproduct Deep Processing School of Food Science and EngineeringCentral South University of Forestry and TechnologyChangsha 410004China State Key Laboratory of drug Research Drug Discovery and Design CenterShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China Department of Geriatrics Third Xiangya HospitalCentral South UniversityChangsha 410013China

Multifunctional molecules with both optical signal and pharmacological activity play an important role in drug development,disease diagnosis,and basic theoretical *** N(APN),as a representative tumor biomarker with anti-tumor potential,still lacks a high-precision theranostic probe specifically targeting *** this study,a novel quaternity design strategy for APN theranostic probe was *** proposed strategy utilizes advanced machine learning and molecular dynamics simulations,and cleverly employs the strategy of conformation-induced fluorescence recovery to achieve multi-objective optimization and integration of functional *** this strategy,a unique“Off-On”theranostic probe,ABTP-DPTB,was ingeniously constructed to light up APN through fluorescence restoration,relying on conformation-induced effects and solvent *** from the common diagnostic probes,the intelligent design with non-substrated linkage makes ABTP-DPTB for long-term in-situ *** fabricated probe was used for detecting and inhibiting APN in various environments,with a better in vitro inhibitory than golden-standard drug bestatin.

关键词： Theranostic probe artificial intelligence Conformational restriction Fluorescence restoration Design strategy

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Advanced deep learning methods for molecular property prediction

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Quantitative Biology 2023年第4期11卷 395-404页

作者： Chao Pang Henry H.Y.Tong Leyi Wei School of Software Shandong UniversityJinanChina Joint SDU-NTU centre forartificial intelligence Research(C-FAIR) Shandong UniversityJinanChina centre for artificial intelligence driven drug discovery FacultyofApplied ScienceMacao Polytechnic UniversityMacaoChina

The prediction of molecular properties is a crucial task in the field of drug *** methods that can accurately predict molecular properties can significantly accelerate the drug discovery process and reduce the cost of drug *** recent years,iterative updates in computing hardware and the rise of deep learning have created a new and effective path for molecular property *** learning methods can leverage the vast amount of data accumulated over the years in drug discovery and do not require complex feature *** this review,we summarize molecular representations and commonly used datasets in molecular property prediction models and present advanced deep learning methods for molecular property prediction,including state-of-the-art deep learning networks such as graph neural networks and Transformer-based models,as well as state-of-the-art deep learning strategies such as 3D pre-train,contrastive learning,multi-task learning,transfer learning,and *** also point out some critical issues such as lack of datasets,low information utilization,and lack of specificity for diseases.

关键词： dataset deep learning molecular property prediction molecular representations

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Phosphorylated protein chip combined with artificial intelligence tools for precise drug screening

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Journal of Biomedical Research 2024年第3期38卷 195-205页

作者： Katsuhisa Horimoto Yuki Suyama Tadamasa Sasaki Kazuhiko Fukui Lili Feng Meiling Sun Yamin Tang Yixuan Zhang Dongyin Chen Feng Han SOCIUM Inc. Tokyo 1350064Japan International Medical Center Saitama Medical UniversitySaitama 350-1298Japan artificial intelligence Research Center National Institute of Advanced Industrial Science and TechnologyTokyo 1350064Japan Department of Informatics and Data Science Sanyo-Onoda City UniversityYamaguchi 7560884Japan International Joint Laboratory for drug Target of Critical Illnesses School of PharmacyNanjing Medical UniversityNanjingJiangsu 211166China Department of Physiology School of Basic Medical SciencesNanjing Medical UniversityNanjingJiangsu 211166China Gusu School Nanjing Medical UniversitySuzhouJiangsu 215001China National Vaccine Innovation Platform Nanjing Medical UniversityNanjingJiangsu 211166China

We have developed a protein array system,named"Phospho-Totum",which reproduces the phosphorylation state of a sample on the *** protein array contains 1471 proteins from 273 known signaling *** to the activation degrees of tyrosine kinases in the sample,the corresponding groups of substrate proteins on the array are phosphorylated under the same *** addition to measuring the phosphorylation levels of the 1471 substrates,we have developed and performed the artificial intelligence-assisted tools to further characterize the phosphorylation state and estimate pathway activation,tyrosine kinase activation,and a list of kinase inhibitors that produce phosphorylation states similar to that of the *** Phospho-Totum system,which seamlessly links and interrogates the measurements and analyses,has the potential to not only elucidate pathophysiological mechanisms in diseases by reproducing the phosphorylation state of samples,but also be useful for drug discovery,particularly for screening targeted kinases for potential drug kinase inhibitors.

关键词： Phospho-Totum protein array signal transduction pathways artificial intelligence tools drug screening

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