Journal of Cheminformatics最新文献

{"title":"Pharmacophore model guided 3D molecular generation through diffusion model","authors":"Bohao Li,Xinyu Wu,Ting Ran,Jingpeng Zhong,Pan He,Yongzhi Lu,Miru Tang,Yuedong Yang,Mingyuan Xu,Jinsai Shang,Hongming Chen","doi":"10.1186/s13321-026-01298-z","DOIUrl":"https://doi.org/10.1186/s13321-026-01298-z","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"140 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MarkitS: an image-to-SMILES parsing workflow for Markush structures","authors":"Kun-Lin Tsai,Yi-Chen Lin,Feng-Chi Chen","doi":"10.1186/s13321-026-01301-7","DOIUrl":"https://doi.org/10.1186/s13321-026-01301-7","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"55 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HSSynergy: scale-aware hierarchical attention for interpretable drug synergy prediction","authors":"Yue-Hua Feng,Shu-Cong Zhang,Xiao-Ying Yan,Jian-Yu Shi","doi":"10.1186/s13321-026-01293-4","DOIUrl":"https://doi.org/10.1186/s13321-026-01293-4","url":null,"abstract":"Abstract Combination therapy offers a promising strategy for cancer treatment by reducing toxicity and overcoming drug resistance. However, existing substructure-based prediction methods may struggle to effectively capture the scale-specific features of substructure interactions and often overlook cell-line-specific substructure selection, which can limit mechanistic interpretability. To address this issue, we propose HSSynergy, a hierarchical substructure-aware deep learning framework for predicting anticancer drug synergy. It first employs Graph Attention-Convolution Fusion Module to adaptively extract multi-scale substructure features from molecular graphs. Rather than indiscriminately mixing features, it introduces a Scale-Aware Masked Attention mechanism that enforces precise layer-wise alignment, and utilizes hierarchical grouping with mask constraints to achieves same-scale focusing while shielding against cross-scale noise. Furthermore, shifting away from passive cell line representations, a Cell-Active Cross-Attention mechanism models the active selection of specific substructures by heterogeneous cancer cells, capturing precise drug-cell contexts. Rigorous evaluations on two benchmark datasets show that HSSynergy achieves superior performance compared to state-of-the-art methods with robust generalization to unseen drugs and cell lines. Beyond predictive metrics, it provides mechanistic interpretability insights, revealing the hierarchical emergence of substructures and accurately pinpointing literature-validated functional groups driving synergy in specific drug combinations. Notably, several novel synergistic combinations predicted by HSSynergy are supported by existing literature and clinical evidence, suggesting its potential utility in aiding anticancer drug discovery. Scientific contribution HSSynergy (i) Scale-Aware Masked Attention restricts substructure interactions within scale groups to reduce cross-scale noise. (ii) Cell-Active Cross-Attention models dynamic, cell-specific substructure selection instead of static cell-line fusion. (iii)Hierarchical attention links synergy predictions to pharmacologically functional groups.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"1 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open-source DNA-encoded library informatics package for design, decoding, and analysis: DELi","authors":"James Wellnitz,Brandon Novy,Shu-Hang Lin,Travis Maxfield,Matthew Axtman,Tina M. Leisner,Eric M. Merten,Ivanna Zhilinskaya,Devan J. Shell,Kellyn M. Schroeder,Jacqueline L. Norris-Drouin,P. Brian Hardy,Kenneth H. Pearce,Konstantin I. Popov","doi":"10.1186/s13321-026-01296-1","DOIUrl":"https://doi.org/10.1186/s13321-026-01296-1","url":null,"abstract":"Abstract Background DNA-encoded library (DEL) technology has become a powerful tool in modern drug discovery. However, fully harnessing its potential requires the use of extensive computational methods, which are often available only through proprietary software. This restricts accessibility for small teams lacking robust informatics support, hindering the growth of the technology. Objective We introduce DELi, an open-source DEL informatics platform developed for library design, next-generation sequencing (NGS) read processing, and binding data analysis. Implementation DELi offers a simple and flexible configuration setup that is readily customizable, while remaining easy to use and supported by a clear user interface. To showcase its capabilities, we used DELi to design a custom benzimidazole-based DEL (UNCDEL006), and performed proof-of-concept selection experiments against bromodomain-containing protein 4 (BRD4). Results The DELi decoding and analysis modules identified top-performing compounds, leading to the off-DNA synthesis of UNC11951. This compound was confirmed as a nanomolar BRD4 binder via isothermal titration calorimetry (ITC) and demonstrated thermal shift stabilization as observed by differential scanning fluorimetry (DSF). These results demonstrate DELi as an effective tool for DEL design and analysis. Availability DELi is written in Python and made available through PyPi and GitHub, alongside the open-sourced UNCDEL006 library. Scientific contribution DELi addresses the need for accessible computational tools for DEL by integrating robust analysis methods within a cohesive, open-source platform. It provides a fully documented and experimentally validated end-to-end workflow, improving reproducibility and lowering the barrier to entry for researchers adopting DEL technology.","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"266 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893659","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"I2M: a box-based detection framework for molecular structure recognition from images","authors":"Bowen Tang,Jing Peng,Tong Wu,WenFan Wu,Wenxing Lv,Ruiquan Ge,Ho Leung Ng,Hongyu Hu,Siyu Zhou,Yuqing Qian","doi":"10.1186/s13321-026-01290-7","DOIUrl":"https://doi.org/10.1186/s13321-026-01290-7","url":null,"abstract":"","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"47 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on: “A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review” (Marzouk et al., 2025)","authors":"Alireza Kargar,&nbsp;Mohammad Ali Zamani,&nbsp;Ghader Mohammadnezhad","doi":"10.1186/s13321-026-01291-6","DOIUrl":"10.1186/s13321-026-01291-6","url":null,"abstract":"<div><p>Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug–drug, drug–disease, and drug–nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely restricted to PubMed with title- and abstract-level filtering, while manual record removal is reported without explicit criteria defining “irrelevant” studies, limiting transparency and reproducibility. Protocol registration, duplicate independent screening, standardized extraction procedures, and formal bias assessment using established frameworks such as ROBIS, PROBAST+AI, and TRIPOD+AI were not clearly reported. The review reports performance metrics such as area under the receiver operating characteristic curve (AUROC), but does not provide a structured framework for interpreting or comparing metrics across heterogeneous datasets, prediction tasks, and evaluation protocols. Because the interpretation of AUROC and precision–recall metrics depends on class prevalence, outcome definition, and the intended prediction task, future reviews should report complementary discrimination metrics, calibration, uncertainty estimates, and external validation rather than assuming that any single metric is universally preferable. Claims of superior model performance should be supported by confidence intervals and statistical comparisons appropriate to the evaluation design, such as paired DeLong testing when applicable. Claims of superior model performance should also be supported by appropriate statistical testing, including methods such as the nonparametric DeLong test. Several conceptual clarifications are also warranted. AI models may prioritize hypotheses but do not replace experimental or clinical validation under current regulatory standards. Furthermore, AUROC should not be conflated with pharmacokinetic area under the curve, and SciBERT should not be characterized as a three-dimensional molecular graph framework. Future reviews should adopt transparent multi-database searches, structured bias assessment, and reproducible reporting practices.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505016/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational applications in secondary metabolite discovery (CAiSMD) 2026: focus on secondary metabolites towards drug discovery","authors":"Fidele Ntie-Kang,&nbsp;Jude Y. Betow,&nbsp;Donatus B. Eni,&nbsp;Jonathan A. Metuge,&nbsp;Albisto Adriano,&nbsp;Mary Anti Chama,&nbsp;Hans Denis Bamal,&nbsp;Philippe Belle Ebanda Kedi,&nbsp;Lívia Mendes Campos,&nbsp;Abubakar Sadiq Abdullahi,&nbsp;Gabriel Francisco Filippin Medina,&nbsp;Seraphin B. N. Mbeket,&nbsp;Yemi A. Adekunle,&nbsp;Alice Rhelly V. Carvalho,&nbsp;Bruno D. A. Akamba,&nbsp;Christianah Adebimpe Dare,&nbsp;Yoshua B. Mtulo,&nbsp;Edgar López-López,&nbsp;Yannick Djoumbou-Feunang,&nbsp;Clovis S. Metuge,&nbsp;Solange Ndamo,&nbsp;Daniel M. Shadrack,&nbsp;Bakary N’tji Diallo,&nbsp;Boris D. Bekono,&nbsp;Marilia Valli,&nbsp;James A. Mbah,&nbsp;Saheed Sabiu,&nbsp;Samuel A. Egieyeh,&nbsp;José L. Medina-Franco,&nbsp;Jutta Ludwig-Müller","doi":"10.1186/s13321-026-01280-9","DOIUrl":"10.1186/s13321-026-01280-9","url":null,"abstract":"<div><h3>Background</h3><p>The <i>Computational Applications in Secondary Metabolite Discovery (CAiSMD) 2026</i> workshop was held from 24 to 26 March 2026 in a hybrid format, combining onsite participation at the University of Buea Center for Drug Discovery with global virtual attendance. Building on previous editions, <i>CAiSMD</i> continues to promote the application of bio/cheminformatics and computational chemistry in natural product-based discovery, with a strong emphasis on capacity building for researchers in resource-limited settings.</p><h3>Methods</h3><p>The scientific program featured three keynote lectures, seven oral presentations, two hands-on training sessions, two roundtable discussions, and a session dedicated to Young Investigators. Key themes included chemoinformatics resources (databases, webservers, packages, codes, etc.) for natural product research, antimicrobial drug discovery, integrated computational bioprospection, virtual screening, network pharmacology, and multi-target drug design. The hands-on sessions provided practical training in chemical space exploration and virtual screening using the Latin American and African natural products databases.</p><h3>Results</h3><p>A major highlight was the Young Investigator session, which showcased innovative research from emerging scientists (MSc students, PhD students, and early postdocs) across diverse therapeutic areas, emphasizing the growing adoption of computational approaches worldwide. Interactive discussions further addressed challenges related to data accessibility, interdisciplinary collaboration, and training needs. Complemented with relevant experimental models, the introduction of bioinformatics, and computational systems biology have accelerated therapeutic innovations through the elimination of time-consuming, expensive, challenging, and inefficient processes that are consistent with the conventional drug discovery systems.</p><h3>Conclusion</h3><p>Overall, <i>CAiSMD2026</i> successfully combined scientific exchange with practical training, reinforcing the role of computational tools in accelerating secondary metabolite research and empowering young and emerging scientists, particularly in developing countries, to contribute to global drug discovery efforts.</p><h3>Scientific contribution</h3><p>This workshop particpates actively in capacity building fo next generation scientists for the use of computational technologies for secondary metabolite discovery with diverse applications.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13504974/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using MR-chordless circuits for efficient enumeration of autocatalytic cores in large chemical reaction networks","authors":"Richard Golnik,&nbsp;Nicola Vassena,&nbsp;Peter F. Stadler,&nbsp;Thomas Gatter","doi":"10.1186/s13321-026-01240-3","DOIUrl":"10.1186/s13321-026-01240-3","url":null,"abstract":"<div><p>Autocatalysis is an important property of chemical reaction networks (CRNs) that is particularly prevalent in metabolic networks. A set of well-defined <i>autocatalytic cores</i> prominently features minimal subsystems that determine the autocatalytic capabilities. Recently, a graph-theoretic characterization has become available that enabled the enumeration of moderate-sized autocatalytic cores in real-life metabolic networks. Such an approach relies on enumerating and properly assembling elementary circuits in the bipartite graph associated to a CRN. Here, we improve on this approach in two ways: (1) We elaborate on algorithms for the enumeration of elementary circuits restricted to so-called MR-chordless circuits. These circuits do not have a chord from a Metabolite to a Reaction vertex, and are the only candidates to find autocatalytic cores. (2) We interleave our new algorithm with tests for autocatalysis to further limit the number of circuits that need to be stored for the construction of autocatalytic cores more complex than elementary MR-chordless circuits. Combined, these innovations achieve a performance gain of several orders of magnitude and make it possible to exhaustively enumerate all autocatalytic cores in real-life metabolic reaction networks comprising several hundred metabolites and reactions. Importantly, we find that reaction networks with irreversible reactions contain complex autocatalytic cores comprising more than a single “cycle with an ear”. Such structures exceed the established classification of autocatalytic cores for fully reversible networks into five types.</p><p><b>Scientific contribution</b></p><p>We developed a new graph-theoretic algorithm for enumerating autocatalytic cores that can handle large genome-scale metabolic models. Implemented in the Python program <span>autogatito</span>, it is up to four orders of magnitude faster than previous methods. Applications to large metabolic network models that involve both reversible and nonreversible reactions reveal that more complex autocatalytic cores exist than predicted by existing classification schemes for reversible reactions.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01240-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design","authors":"Lucas Leuschner,&nbsp;Oscar Palomino-Hernandez","doi":"10.1186/s13321-026-01273-8","DOIUrl":"10.1186/s13321-026-01273-8","url":null,"abstract":"<div><p>Inverse molecular design imposes multiple property constraints, requiring them to be combined into a scalar reward for reinforcement-learning (RL) fine-tuning; a step that can be highly sensitive to the reward formulation. Here, we systematically analyze how scalarization choices and stabilization mechanisms shape optimization dynamics in RL fine-tuning for multi-parameter optimization (MPO). We evaluate three scalarization schemes (arithmetic mean, geometric mean, and Chebyshev) under multiple target regimes that vary in constraint tightness and prior support. While broad targets yield stable behavior across scalarizations, narrow and weakly supported targets expose sharp failure modes. We further show that these failure modes can be prevented through the usage of target-aware weight scheduling. Collectively, our results highlight critical interactions between scalarization choice and weight dynamics, and provide mechanistic insight and actionable guidance for stabilizing RL fine-tuning in constrained molecular MPO.</p><p><b>Scientific contribution</b></p><p>This work examines different reward formulations in multi-objective reinforcement learning for molecular design, going beyond the designs typically considered in prior studies. We further show how these formulations influence convergence behavior and collapse modes, and how their effects depend on the size and constraint structure of the multi-dimensional optimization space.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01273-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"pyMSscreen: an interactive interface for automated quality control and prescreening in mass spectrometry workflows","authors":"Anjana Elapavalore,&nbsp;Parviel Chirsir,&nbsp;Emma H. Palm,&nbsp;Martin Jakubec,&nbsp;Emma L. Schymanski","doi":"10.1186/s13321-026-01260-z","DOIUrl":"10.1186/s13321-026-01260-z","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Reliable quality control of mass spectrometry data remains a major bottleneck in target, suspect and non-target screening workflows. In non-target analysis, thousands of features can be extracted through automated peak picking, many of which require manual verification to exclude artefacts arising from noise, co-elution, inconsistent chromatographic behavior or statistical prioritization. Similarly, suspect screening workflows depend on effective chromatographic and spectral quality control to support confidence in annotated features. However, existing quality control practices are often fragmented across vendor software and custom scripts, limiting reproducibility and scalability. This article describes pyMSscreen, a unified framework that enables automated and reproducible chromatographic quality control across target, suspect, and non-target mass spectrometry workflows. The backend leverages RDKit and pyOpenMS for compound validation and spectral extraction, while the frontend provides a web-based interface for systematic inspection of chromatograms and mass spectra. The development of pyMSscreen draws on experience gained from the earlier R-based Shinyscreen, which showed that accessible graphical user interface can greatly simplify the review of liquid chromatography mass spectrometry data. However, the practical limitations of the Shiny framework, including difficulties with deployment, motivated the creation of a more flexible and modern solution. pyMSscreen employs a modular architecture that supports target, suspect, and non-target screening by accommodating both SMILES or &lt;i&gt;m/z&lt;/i&gt; only input lists. Automated quality control metrics are applied consistently across workflows, enabling assessment of peak shape, signal intensity, retention-time alignment, and background noise prior to downstream interpretation. Container-based deployment ensures reproducible installation and execution across computational environments. The performance and applicability of pyMSscreen is illustrated using a representative non-target dataset and suspect screening workflow demonstrating consistent results relative to established approaches, while substantially reducing manual data handling and review time. Overall, pyMSscreen provides an efficient, transparent, and reproducible platform for chromatographic quality control and data curation in high resolution mass spectrometry based cheminformatics workflows. The code is openly available (Artistic 2.0 license) at https://gitlab.com/uniluxembourg/lcsb/eci/pymsscreen.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Scientific contribution&lt;/b&gt;&lt;/p&gt;&lt;p&gt;pyMSscreen presents a unified framework for chromatographic quality control that integrates automated quality metrics with interactive visual inspection of mass spectrometry data. The central contribution is the consistent evaluation of chromatographic peak quality and spectral alignment across target, suspect, and non-target screening workflows within a single methodology. This work builds on earl","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"18 1","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13321-026-01260-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}