Digital discovery最新文献

MAARDTI: a multi-perspective attention aggregation model for the prediction of drug–target interactions MAARDTI：用于药物-靶标相互作用预测的多角度注意力聚集模型

IF 6.2

Digital discovery Pub Date : 2025-09-16 DOI: 10.1039/D5DD00311C

Xinke Zhan, Tiantao Liu, Changqing Yu, Yu-An Huang, Zhuhong You and Shirley W. I. Siu

{"title":"MAARDTI: a multi-perspective attention aggregation model for the prediction of drug–target interactions","authors":"Xinke Zhan, Tiantao Liu, Changqing Yu, Yu-An Huang, Zhuhong You and Shirley W. I. Siu","doi":"10.1039/D5DD00311C","DOIUrl":"https://doi.org/10.1039/D5DD00311C","url":null,"abstract":"Accurate prediction of drug–target interactions (DTIs) is indispensable for discovering novel drugs and repositioning existing ones. Recently, numerous methods based on deep learning have made promising progress in DTI predictions. These methods often utilize a single attention mechanism, which limits their ability to capture the complex features of both drugs and proteins. As a result, feature representation can be incomplete, training can become more complex and prone to overfitting. These together can impair the generalizability of the model. To address these problems, we propose an end-to-end neural network drug–target interaction approach called Multi-perspective Attention AggRegating (MAARDTI). Here, a multi-perspective attention mechanism is introduced that combines channel attention and spatial attention to capture a more comprehensive feature representation. The dual-context refocusing module is used to enhance the attention representation capability and improve the generalizability of the model. Experiments show that our proposed model outperforms ten state-of-the-art methods in three public datasets, achieving AUC values of 0.8975, 0.9248, and 0.9330 in DrugBank, Davis and KIBA, respectively. In the cold-splitting test with novel targets, drugs, and their bindings, MAARDTI performs on par with some methods for cold drug predictions. It outperforms in predicting unseen targets and bindings, underscoring the effectiveness of the novel multi-perspective attention mechanism in challenging scenarios. Hence, MAARDTI has the potential to serve as an effective tool for rapid identification of novel DTIs in drug research.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 2994-3007"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00311c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital design and discovery of biological metal–organic frameworks for gas signaling 气体信号生物金属-有机框架的数字设计与发现

IF 6.2

Digital discovery Pub Date : 2025-09-16 DOI: 10.1039/D5DD00213C

Yifei Yue, Athulya S. Palakkal, Saad Aldin Mohamed and Jianwen Jiang

{"title":"Digital design and discovery of biological metal–organic frameworks for gas signaling","authors":"Yifei Yue, Athulya S. Palakkal, Saad Aldin Mohamed and Jianwen Jiang","doi":"10.1039/D5DD00213C","DOIUrl":"https://doi.org/10.1039/D5DD00213C","url":null,"abstract":"Metal–organic frameworks (MOFs) are intriguing nanoporous materials with a wide variety of potential applications. Recent efforts in extending the functionalities of MOFs toward biological applications have inspired the development of Bio-MOFs comprising biological building blocks. Yet, while numerous experimental studies have attempted to synthesize different Bio-MOFs, computational screening of Bio-MOFs is impeded by the limited number of Bio-MOFs currently available. Here, we design a Bio-hMOF database containing 17 681 hypothetical structures, assembled from the fragments of 309 experimental Bio-MOFs, with rigorous geometry optimization and structural checks. Subsequently, a possible biological application of the Bio-hMOFs is demonstrated for the selective adsorption of signaling gases NO and CO. The effects of different inorganic and organic fragments on the mechanical properties of Bio-hMOFs are also examined. Finally, we identify mechanically stable Bio-hMOFs promising for selective NO/CO adsorption and holistically analyze the trade-off between adsorption capacity and mechanical strength. The digital Bio-hMOF database is available publicly, in which future studies can be leveraged to discover top candidates and unveil new structure–property insights into the further design of Bio-MOFs for targeted biological applications.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 3008-3017"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00213c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning workflow to accelerate the design of in vitro release tests from liposomes 加速脂质体体外释放试验设计的机器学习工作流程

IF 6.2

Digital discovery Pub Date : 2025-09-15 DOI: 10.1039/D5DD00112A

Daniel Yanes, Vasiliki Paraskevopoulou, Heather Mead, James Mann, Magnus Röding, Maryam Parhizkar, Cameron Alexander, Jamie Twycross and Mischa Zelzer

{"title":"A machine learning workflow to accelerate the design of in vitro release tests from liposomes","authors":"Daniel Yanes, Vasiliki Paraskevopoulou, Heather Mead, James Mann, Magnus Röding, Maryam Parhizkar, Cameron Alexander, Jamie Twycross and Mischa Zelzer","doi":"10.1039/D5DD00112A","DOIUrl":"https://doi.org/10.1039/D5DD00112A","url":null,"abstract":"Liposomes are amongst the most promising and versatile nanomedicine products employed in recent years. In vitro release (IVR) tests are critical during development of new liposome-based products. The drug release characteristics of a formulation are affected by multiple factors related to the formulation itself and the IVR method used. While the effect of some of these parameters has been explored, their relative importance and contribution to the final drug release profile are not sufficiently understood to enable rational design choices. This prolongs the development and approval of new medicines. In this study, a machine learning workflow is developed which can be used to better understand patterns in liposome formulation properties, IVR methods, and the resulting drug release characteristics. A comprehensive database of liposome release profiles, including formulation properties, IVR method parameters, and drug release profiles is compiled from academic publications. A classification model is developed to predict the release profile type (kinetic class), with a significant increase in the balanced accuracy test score compared to a random baseline. The resulting machine learning approach enhances understanding of the complex liposome drug release dynamics and provides a predictive tool to accelerate the design of liposome IVR tests.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 2983-2993"},"PeriodicalIF":6.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00112a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents 用机器学习预测水和有机溶解度：识别有机共溶剂的工作流程

IF 6.2

Digital discovery Pub Date : 2025-09-15 DOI: 10.1039/D5DD00134J

Maurycy Krzyżanowski, Sirazam Munira Aishee, Nirala Singh and Bryan R. Goldsmith

{"title":"Predicting aqueous and organic solubilities with machine learning: a workflow for identifying organic cosolvents","authors":"Maurycy Krzyżanowski, Sirazam Munira Aishee, Nirala Singh and Bryan R. Goldsmith","doi":"10.1039/D5DD00134J","DOIUrl":"https://doi.org/10.1039/D5DD00134J","url":null,"abstract":"Developing predictive models of solubility is useful for accelerating solvent selection for applications ranging from electrochemical conversion of organics to pharmaceutical drug development. Herein, we report on the development of a machine learning (ML) workflow for identifying organic cosolvents to increase the concentration of hydrophobic molecules in aqueous mixtures. This task is of particular interest for the electrocatalytic conversion of biomass and bio-oils into sustainable fuels, which faces challenges due to the low aqueous solubility of the feedstock. First, we predict the miscibility of potential cosolvents in water, and we only consider cosolvents that are miscible. Second, we rank cosolvents based on the predicted solubility of the molecule of interest in them. To achieve this, we train two separate ML models: one using the AqSolDB dataset to predict aqueous solubility, and another using the BigSolDB dataset to predict solubility in organic solvents. We select the Light Gradient Boosting Machine (LGBM) model architecture for aqueous solubility (test R2 = 0.864, RMSE = 0.851 for log(S (mol−1 dm−3))) and organic solubility (test R2 = 0.805, RMSE = 0.511 for log(x)) predictions based on comparing different ML models and features. We examine the generalizability of the organic solubility model on unseen solutes both quantitatively and qualitatively. We evaluate the utility of this ML workflow by identifying cosolvents for benzaldehyde and limonene—two hydrophobic molecules that are relevant for sustainable fuel production—and validate our predictions via experimental solubility measurements.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 3031-3042"},"PeriodicalIF":6.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00134j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states. 基于图的反应势垒高度预测与跃迁态的动态预测。

IF 6.2

Digital discovery Pub Date : 2025-09-15 DOI: 10.1039/d5dd00240k

Johannes Karwounopoulos, Jasper De Landsheere, Leonard Galustian, Tobias Jechtl, Esther Heid

{"title":"Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states.","authors":"Johannes Karwounopoulos, Jasper De Landsheere, Leonard Galustian, Tobias Jechtl, Esther Heid","doi":"10.1039/d5dd00240k","DOIUrl":"10.1039/d5dd00240k","url":null,"abstract":"The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product structures were shown to provide promising accuracies for reaction property prediction. They rely solely on molecular graph changes as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly for organic reactions. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for the two investigated datasets RDB7 and RGD1.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12461184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145187731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

“Twisting” the data: a universal machine-learning approach to classify single-molecule curves and beyond “扭曲”数据：一种通用的机器学习方法，用于分类单分子曲线及其他

IF 6.2

Digital discovery Pub Date : 2025-09-15 DOI: 10.1039/D5DD00207A

C. Roldán-Piñero, M. Teresa González, Pablo M. Olmos, Linda A. Zotti and Edmund Leary

{"title":"“Twisting” the data: a universal machine-learning approach to classify single-molecule curves and beyond","authors":"C. Roldán-Piñero, M. Teresa González, Pablo M. Olmos, Linda A. Zotti and Edmund Leary","doi":"10.1039/D5DD00207A","DOIUrl":"https://doi.org/10.1039/D5DD00207A","url":null,"abstract":"We present a new automated supervised procedure trained to classify both conductance-voltage (G(V)) curves and conductance-distance (G(z)) traces generated in single-molecule junctions to a high degree of confidence. Compared to unsupervised methods, our approach, involving a convolutional neural network (CNN), is vastly superior as it allows core shapes to be recognised by ignoring differences in scale and is relatively insensitive to conductance jumps. A key aspect is the transformation of curves into a spiral image map, which allows us to separate various fundamental G(V) and G(z) shapes from datasets containing tens of thousands of curves. Moreover, by using transfer learning, training requires little input data compared to other approaches. This is extremely advantageous as it reduces training time by many orders of magnitude and means the model can be trained on user-selected shapes, including rare types. This contrasts with arbitrary class-assignment, instead basing classification on a sound physical understanding of the system. Furthermore, as there is no minimum class population requirement, our method can be used to find rare events with a high degree of confidence. As an example, we used our procedure to find, with a minimum 66% confidence level, a class of G(V) curves which are parabolic at low bias but flat at high bias. Such curves make up just 4% of the total, and would be very difficult to detect cleanly with unsupervised methods. This gives insights into the electron transport behaviour at high-bias because we can now easily quantify the types of curves present. Thanks to its universality, this opens up new possibilities in general signal processing and the identification of rare and important events.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 3043-3052"},"PeriodicalIF":6.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00207a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond training data: how elemental features enhance ML-based formation energy predictions 超越训练数据：元素特征如何增强基于ml的地层能量预测

IF 6.2

Digital discovery Pub Date : 2025-09-04 DOI: 10.1039/D5DD00182J

Hamed Mahdavi, Vasant Honavar and Dane Morgan

引用次数: 0

Accelerated sol–gel synthesis of nanoporous silica via integrated small angle X-ray scattering with an open-source automation platform 基于集成小角x射线散射的纳米多孔二氧化硅溶胶-凝胶加速合成

IF 6.2

Digital discovery Pub Date : 2025-09-04 DOI: 10.1039/D5DD00274E

Brenden Pelkie, Chi Yuet Yung, Zachery R. Wylie and Lilo D. Pozzo

{"title":"Accelerated sol–gel synthesis of nanoporous silica via integrated small angle X-ray scattering with an open-source automation platform","authors":"Brenden Pelkie, Chi Yuet Yung, Zachery R. Wylie and Lilo D. Pozzo","doi":"10.1039/D5DD00274E","DOIUrl":"https://doi.org/10.1039/D5DD00274E","url":null,"abstract":"Sol–gel syntheses can produce diverse arrays of nanomaterials, including mesoporous colloidal silica, within chemical design spaces that can become exceedingly large, complex, and expensive to explore via traditional methods. A new workflow for sol–gel automated synthesis of SiO2, based on the open-hardware platform Science-Jubilee with integrated small angle X-ray scattering (SAXS), is demonstrated to outline correlations between precursor concentrations and morphological properties including particle size, polydispersity, extent of internal porosity and type of pore-phase order. Development of open and accessible high-throughput experimentation approaches is critical to accelerating the application of bespoke mesoporous silica nanostructures for potential use in chemical separations, catalysis, and drug delivery among other fields. The new hardware and workflow adapts and extends the Science-Jubilee automation platform for sol–gel synthesis and also integrates the NIST-design for the autonomous formulation laboratory (NIST-AFL) to achieve in situ structural characterization using either synchrotron and/or laboratory small-angle X-ray scattering (SAXS) instruments. An experimental campaign for SiO2 room-temperature sol–gel synthesis using cetyltrimethylammonium bromide (CTAB) and Pluronic F127 surfactants, ammonia and tetraethyl orthosilicate (TEOS), demonstrates that it can reproducibly yield colloidal silica of variable size, dispersity, and internal pore phase order. The results also correlate well with published synthetic outcomes using traditional manual methods.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 3018-3030"},"PeriodicalIF":6.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00274e?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An open-source peristaltic pump with multiple independent channels for laboratory automation 具有多个独立通道的开源蠕动泵，用于实验室自动化

IF 6.2

Digital discovery Pub Date : 2025-09-04 DOI: 10.1039/D5DD00157A

Michael Buchhorn, Gun Deniz Akkoc and Dominik Dworschak

{"title":"An open-source peristaltic pump with multiple independent channels for laboratory automation","authors":"Michael Buchhorn, Gun Deniz Akkoc and Dominik Dworschak","doi":"10.1039/D5DD00157A","DOIUrl":"https://doi.org/10.1039/D5DD00157A","url":null,"abstract":"In recent years laboratory automation, high throughput characterization and self-driving laboratories have emerged as promising tools to accelerate the process of researching and developing novel materials. Many of these automated setups rely on precise and reliable liquid handling to perform their large-scale studies. Peristaltic pumps, with their simple and robust design, a low price point and only the tube itself being in contact with the fluid, are well suited to power these increasingly more complex liquid handling tasks. While existing open-source designs of peristaltic pumps already feature multiple channels to accommodate the need for more fluid lines, these channels are all powered by a single motor and can therefore not run independently of each other, reducing their usability and versatility. To overcome this limitation, we developed an open-source peristaltic pump with four fully independent pumping channels, a quick-swap cassette system and an automation friendly SiLA 2 interface. The design was created with lab automation and self-driving laboratories in mind and allows for flow rates from 0.3 μL min−1 to 8 mL min−1 with a repeatability of 0.2%. Another focus of the design was accessibility, with the pump built from 3D-printed parts and commonly available standardized and off-the-shelf hardware components, resulting in an affordable price point of around 280 USD.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 2864-2875"},"PeriodicalIF":6.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00157a?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI-powered inverse design for tailored narrowband molecular emitters 窄带分子发射器的生成式人工智能逆向设计

IF 6.2

Digital discovery Pub Date : 2025-09-04 DOI: 10.1039/D5DD00268K

Mianzhi Pan, Tianhao Tan, Yawen Ouyang, Qian Jin, Yougang Chu, Wei-Ying Ma, Jianbing Zhang, Lian Duan, Dong Wang and Hao Zhou

{"title":"Generative AI-powered inverse design for tailored narrowband molecular emitters","authors":"Mianzhi Pan, Tianhao Tan, Yawen Ouyang, Qian Jin, Yougang Chu, Wei-Ying Ma, Jianbing Zhang, Lian Duan, Dong Wang and Hao Zhou","doi":"10.1039/D5DD00268K","DOIUrl":"https://doi.org/10.1039/D5DD00268K","url":null,"abstract":"As organic display technology progresses, the urgent and daunting challenge lies in the development of next-generation molecular emitters capable of delivering an extensive color gamut with unparalleled color purity. The existing process for uncovering new emitters is largely reliant on a time-consuming and costly trial-and-error method. However, with the integration of AI, the pace of materials discovery is accelerated dramatically. Here, a molecular generation framework, MEMOS, which harnesses the efficiency of Markov molecular sampling techniques alongside multi-objective optimization for the inverse design of molecules, is presented. MEMOS facilitates the precise engineering of molecules capable of emitting narrow spectral bands at desired colors. Utilizing a self-improving iterative process, it can efficiently traverse millions of molecular structures within hours, pinpointing thousands of target emitters with an impressive success rate up to 80%, as validated by density functional theory calculations. Through the use of MEMOS, well-documented multiple resonance cores from the experimental literature have been successfully retrieved, and a broader color gamut has been achieved with the newly identified tricolor narrowband emitters. These findings underscore the immense potential of MEMOS as an efficient tool for expediting the exploration of the uncharted chemical territory of molecular emitters and their experimental discovery.","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 10","pages":" 2942-2953"},"PeriodicalIF":6.2,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00268k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145236692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0