Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays ...Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.展开更多
Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it ...Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.展开更多
The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the exis...The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.展开更多
BACKGROUND Enhanced recovery after surgery(ERAS),a multidisciplinary and multimodal perioperative care protocol,has been widely used in several surgical fields.However,the effect of this care protocol on liver transpl...BACKGROUND Enhanced recovery after surgery(ERAS),a multidisciplinary and multimodal perioperative care protocol,has been widely used in several surgical fields.However,the effect of this care protocol on liver transplant recipients with endstage liver disease remains unclear.AIM To compare the clinical outcomes of the ERAS protocol and standard care(SC)for liver transplant recipients with end-stage liver disease.METHODS PubMed,Web of Science,Cochrane Library,and EMBASE databases were systematically searched to identify literature reporting the effects of the ERAS protocol on clinical outcomes in patients undergoing liver transplant recipients with endstage liver disease.All articles published to January 1,2025 were searched,followed by data extraction of the included literature and independent quality assessment.Then pooled mean difference(MD)and odds ratio(OR)with a 95%confidence interval(CI)were calculated by either a random-effects or fixed-effects model.RESULTS Overall,eight relevant studies(including two randomized controlled trials,two prospective cohort studies,and four retrospective cohort studies)involving 1220 patients(704 patients in the ERAS group and 516 patients in the SC group).The primary outcomes evaluated included intensive care unit(ICU)stay duration,hospital length of stay,overall complication rates,mortality,and 30-day readmission rates.Our findings showed that ERAS protocols significantly reduced ICU stay duration(MD:-1.21 days,95%CI:-2.08 to-0.34;P=0.006),hospital length of stay(MD:-4.91 days,95%CI:-7.45 to-2.37;P=0.0002),overall complication rates(OR=0.32,95%CI:0.22-0.46;P<0.0001),and mortality(OR=0.57,95%CI:0.33-0.98;P=0.04).However,ERAS was associated with an increased 30-day readmission rate(OR=3.20,95%CI:1.54-6.67;P=0.003).CONCLUSION The current meta-analysis indicated that ERAS protocols can significantly improve short-term clinical outcomes in liver transplant recipients,although the increased readmission rate requires further investigation.Future studies should aim to refine ERAS protocols and explore their long-term efficacy and underlying mechanisms.展开更多
Transition-metal-catalyzed tandem cross-coupling reactions can rapidly construct complex molecules,but they often suffer from site-and regio-selectivity issues.Here,we designed a novel nickel-catalyzed three-component...Transition-metal-catalyzed tandem cross-coupling reactions can rapidly construct complex molecules,but they often suffer from site-and regio-selectivity issues.Here,we designed a novel nickel-catalyzed three-component cross-electrophile coupling(cXEC)platform that enables access to valuable gem-difluoroalkenes.This multicomponent reaction proceeds through a chemoselective alkenylation of aryl halides,followed by alkylation of α-(trifluoromethyl)styrenes,providing a streamlined pathway towards this kind of building blocks.展开更多
Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local an...Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.展开更多
基金supported by the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Science and Technology Program(No.JCYJ20230807140709020)+2 种基金National Natural Science Foundation of China(Nos.62402489,U22A2041,and 62373172)the China Postdoctoral Science Foundation(No.2023M743688)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515011960 and 2023A1515110570)。
文摘Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.
基金financial support by Sichuan Science and Technology,China(No.2023YFG0070).
文摘Fuel cell electric vehicles hold great promise for a diverse range of applications in reducing greenhouse gas emissions.In power fuel cell systems,hydrogen fuel serves as an energy vector.To ensure its suitability,it is necessary for the quality of hydrogen to adhere to the standards set by ISO 14687:2019,which sets maximum limits for 14 impurities in hydrogen,aiming to prevent any degradation of fuel cell performance.Ammonia(NH_(3))is a prominent pollutant in fuel cells,and accurate measurements of its concentration are crucial for hydrogen fuel cell quantity.In this study,a novel detection platform was developed for determining NH_(3)in real hydrogen samples.The online analysis platform integrates a self-developed online dilution module with a Fourier transform infrared spectrometer(ODM-FTIR).The ODM-FTIR can be operated fully automatically with remote operation.Under the optimum conditions,this method achieved a wide linear range between(50∼1000)nmol/mol.The limit of detection(LOD)was as low as 2 nmol/mol with a relative standard deviation(RSD,n=7)of 3.6%at a content of 50 nmol/mol.To ensure that the quality of the hydrogen products meets the requirement of proton exchange membrane fuel cell vehicles(PEMFCV),the developed ODM-FTIR system was applied to monitor the NH_(3)content in Chengdu Hydrogen Energy Co.,Ltd.for 21 days during Chengdu 2021 FISU World University Games.The proposed method retains several unique advantages,including a low detection limit,excellent repeatability,high accuracy,high speed,good stability,and calibration flexibility.It is an effective analytical method for accurately quantifying NH_(3)in hydrogen,especially suitable for online analysis.It also provides a new idea for the analysis of other impurity components in hydrogen.
基金funded by the State Grid Corporation Science and Technology Project(5108-202218280A-2-391-XG).
文摘The high proportion of uncertain distributed power sources and the access to large-scale random electric vehicle(EV)charging resources further aggravate the voltage fluctuation of the distribution network,and the existing research has not deeply explored the EV active-reactive synergistic regulating characteristics,and failed to realize themulti-timescale synergistic control with other regulatingmeans,For this reason,this paper proposes amultilevel linkage coordinated optimization strategy to reduce the voltage deviation of the distribution network.Firstly,a capacitor bank reactive power compensation voltage control model and a distributed photovoltaic(PV)activereactive power regulationmodel are established.Additionally,an external characteristicmodel of EVactive-reactive power regulation is developed considering the four-quadrant operational characteristics of the EVcharger.Amultiobjective optimization model of the distribution network is then constructed considering the time-series coupling constraints of multiple types of voltage regulators.A multi-timescale control strategy is proposed by considering the impact of voltage regulators on active-reactive EV energy consumption and PV energy consumption.Then,a four-stage voltage control optimization strategy is proposed for various types of voltage regulators with multiple time scales.Themulti-objective optimization is solved with the improvedDrosophila algorithmto realize the power fluctuation control of the distribution network and themulti-stage voltage control optimization.Simulation results validate that the proposed voltage control optimization strategy achieves the coordinated control of decentralized voltage control resources in the distribution network.It effectively reduces the voltage deviation of the distribution network while ensuring the energy demand of EV users and enhancing the stability and economic efficiency of the distribution network.
文摘BACKGROUND Enhanced recovery after surgery(ERAS),a multidisciplinary and multimodal perioperative care protocol,has been widely used in several surgical fields.However,the effect of this care protocol on liver transplant recipients with endstage liver disease remains unclear.AIM To compare the clinical outcomes of the ERAS protocol and standard care(SC)for liver transplant recipients with end-stage liver disease.METHODS PubMed,Web of Science,Cochrane Library,and EMBASE databases were systematically searched to identify literature reporting the effects of the ERAS protocol on clinical outcomes in patients undergoing liver transplant recipients with endstage liver disease.All articles published to January 1,2025 were searched,followed by data extraction of the included literature and independent quality assessment.Then pooled mean difference(MD)and odds ratio(OR)with a 95%confidence interval(CI)were calculated by either a random-effects or fixed-effects model.RESULTS Overall,eight relevant studies(including two randomized controlled trials,two prospective cohort studies,and four retrospective cohort studies)involving 1220 patients(704 patients in the ERAS group and 516 patients in the SC group).The primary outcomes evaluated included intensive care unit(ICU)stay duration,hospital length of stay,overall complication rates,mortality,and 30-day readmission rates.Our findings showed that ERAS protocols significantly reduced ICU stay duration(MD:-1.21 days,95%CI:-2.08 to-0.34;P=0.006),hospital length of stay(MD:-4.91 days,95%CI:-7.45 to-2.37;P=0.0002),overall complication rates(OR=0.32,95%CI:0.22-0.46;P<0.0001),and mortality(OR=0.57,95%CI:0.33-0.98;P=0.04).However,ERAS was associated with an increased 30-day readmission rate(OR=3.20,95%CI:1.54-6.67;P=0.003).CONCLUSION The current meta-analysis indicated that ERAS protocols can significantly improve short-term clinical outcomes in liver transplant recipients,although the increased readmission rate requires further investigation.Future studies should aim to refine ERAS protocols and explore their long-term efficacy and underlying mechanisms.
基金financial support from the National Natural Science Foundation of China(Nos.22071101 and 22271147)China Postdoctoral Science Foundation(Nos.2021T140309 and 2021M691511)Natural Science Foundation of Jiangsu Province(No.BK20230771).
文摘Transition-metal-catalyzed tandem cross-coupling reactions can rapidly construct complex molecules,but they often suffer from site-and regio-selectivity issues.Here,we designed a novel nickel-catalyzed three-component cross-electrophile coupling(cXEC)platform that enables access to valuable gem-difluoroalkenes.This multicomponent reaction proceeds through a chemoselective alkenylation of aryl halides,followed by alkylation of α-(trifluoromethyl)styrenes,providing a streamlined pathway towards this kind of building blocks.
基金the National Natural Science Foundation of China(Grant Nos.:62272288,U22A2041)Fundamental Research Funds for the Central Universities,Shaanxi Normal University(Grant No.:GK202302006)the Scientific Research Fund of Hunan Provincial Education Department of China(Grant No.:22B0097).
文摘Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases.
