Journal of Cheminformatics最新文献

{"title":"Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture","authors":"Kohulan Rajan,&nbsp;Henning Otto Brinkhaus,&nbsp;Achim Zielesny,&nbsp;Christoph Steinbeck","doi":"10.1186/s13321-024-00872-7","DOIUrl":"10.1186/s13321-024-00872-7","url":null,"abstract":"<p>Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00872-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141537384","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":"PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models","authors":"Morgan Thomas,&nbsp;Mazen Ahmad,&nbsp;Gary Tresadern,&nbsp;Gianni de Fabritiis","doi":"10.1186/s13321-024-00866-5","DOIUrl":"10.1186/s13321-024-00866-5","url":null,"abstract":"<div><p>SMILES-based generative models are amongst the most robust and successful recent methods used to augment drug design. They are typically used for complete de novo generation, however, scaffold decoration and fragment linking applications are sometimes desirable which requires a different grammar, architecture, training dataset and therefore, re-training of a new model. In this work, we describe a simple procedure to conduct constrained molecule generation with a SMILES-based generative model to extend applicability to scaffold decoration and fragment linking by providing SMILES prompts, without the need for re-training. In combination with reinforcement learning, we show that pre-trained, decoder-only models adapt to these applications quickly and can further optimize molecule generation towards a specified objective. We compare the performance of this approach to a variety of orthogonal approaches and show that performance is comparable or better. For convenience, we provide an easy-to-use python package to facilitate model sampling which can be found on GitHub and the Python Package Index.</p><p><b>Scientific contribution</b></p><p>This novel method extends an autoregressive chemical language model to scaffold decoration and fragment linking scenarios. This doesn’t require re-training, the use of a bespoke grammar, or curation of a custom dataset, as commonly required by other approaches.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00866-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141521629","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":"Application of machine reading comprehension techniques for named entity recognition in materials science","authors":"Zihui Huang,&nbsp;Liqiang He,&nbsp;Yuhang Yang,&nbsp;Andi Li,&nbsp;Zhiwen Zhang,&nbsp;Siwei Wu,&nbsp;Yang Wang,&nbsp;Yan He,&nbsp;Xujie Liu","doi":"10.1186/s13321-024-00874-5","DOIUrl":"10.1186/s13321-024-00874-5","url":null,"abstract":"<div><p>Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to convert the sequence labeling task into a machine reading comprehension (MRC) task. MRC method effectively can solve the challenge of extracting multiple overlapping entities by transforming it into the form of answering multiple independent questions. Moreover, the MRC framework allows for a more comprehensive understanding of the contextual information and semantic relationships within materials science literature, by integrating prior knowledge from queries. State-of-the-art (SOTA) performance was achieved on the Matscholar, BC4CHEMD, NLMChem, SOFC, and SOFC-Slot datasets, with F1-scores of 89.64%, 94.30%, 85.89%, 85.95%, and 71.73%, respectively in MRC approach. By effectively utilizing semantic information and extracting nested entities, this approach holds great significance for knowledge extraction and data analysis in the field of materials science, and thus accelerating the development of material science.</p><p><b>Scientific contribution</b></p><p>We have developed an innovative NER method that enhances the efficiency and accuracy of automatic entity extraction in the field of materials science by transforming the sequence labeling task into a MRC task, this approach provides robust support for constructing knowledge graphs and other data analysis tasks.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00874-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141490382","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":"CPSign: conformal prediction for cheminformatics modeling","authors":"Staffan Arvidsson McShane,&nbsp;Ulf Norinder,&nbsp;Jonathan Alvarsson,&nbsp;Ernst Ahlberg,&nbsp;Lars Carlsson,&nbsp;Ola Spjuth","doi":"10.1186/s13321-024-00870-9","DOIUrl":"10.1186/s13321-024-00870-9","url":null,"abstract":"<div><p>Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptors are also supported. The main modeling methodology is support vector machines (SVMs), but additional modeling methods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efficiency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approaches including random forest, and a directed message-passing neural network. The results show that CPSign produces robust predictive performance with comparative predictive efficiency, with superior runtime and lower hardware requirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input files, perform descriptor calculation and modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet flexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign.</p><p><b>Scientific contribution</b></p><p> CPSign provides a single software that allows users to perform data preprocessing, modeling and make predictions directly on chemical structures, using conformal and probabilistic prediction. Building and evaluating new models can be achieved at a high abstraction level, without sacrificing flexibility and predictive performance—showcased with a method evaluation against contemporary modeling approaches, where CPSign performs on par with a state-of-the-art deep learning based model.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00870-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462387","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":"AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry","authors":"Lung-Yi Chen,&nbsp;Yi-Pei Li","doi":"10.1186/s13321-024-00869-2","DOIUrl":"10.1186/s13321-024-00869-2","url":null,"abstract":"<p>This paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry. Recent advances in artificial intelligence have expanded the application of machine learning in chemistry, particularly in yield prediction, retrosynthesis, and reaction condition prediction. However, the effectiveness of these models hinges on the integrity of chemical reaction datasets, which are often plagued by inconsistencies like missing reactants, incorrect atom mappings, and outright erroneous reactions. AutoTemplate introduces a two-stage approach to refine these datasets. The first stage involves extracting meaningful reaction transformation rules and formulating generic reaction templates using a simplified SMARTS representation. This simplification broadens the applicability of templates across various chemical reactions. The second stage is template-guided reaction curation, where these templates are systematically applied to validate and correct the reaction data. This process effectively amends missing reactant information, rectifies atom-mapping errors, and eliminates incorrect data entries. A standout feature of AutoTemplate is its capability to concurrently identify and correct false chemical reactions. It operates on the premise that most reactions in datasets are accurate, using these as templates to guide the correction of flawed entries. The protocol demonstrates its efficacy across a range of chemical reactions, significantly enhancing dataset quality. This advancement provides a more robust foundation for developing reliable machine learning models in chemistry, thereby improving the accuracy of forward and retrosynthetic predictions. AutoTemplate marks a significant progression in the preprocessing of chemical reaction datasets, bridging a vital gap and facilitating more precise and efficient machine learning applications in organic synthesis.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00869-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462625","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":"Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms","authors":"Elena Bandini,&nbsp;Rodrigo Castellano Ontiveros,&nbsp;Ardiana Kajtazi,&nbsp;Hamed Eghbali,&nbsp;Frédéric Lynen","doi":"10.1186/s13321-024-00873-6","DOIUrl":"10.1186/s13321-024-00873-6","url":null,"abstract":"<div><p>Temperature-responsive liquid chromatography (TRLC) offers a promising alternative to reversed-phase liquid chromatography (RPLC) for environmentally friendly analytical techniques by utilizing pure water as a mobile phase, eliminating the need for harmful organic solvents. TRLC columns, packed with temperature-responsive polymers coupled to silica particles, exhibit a unique retention mechanism influenced by temperature-induced polymer hydration. An investigation of the physicochemical parameters driving separation at high and low temperatures is crucial for better column manufacturing and selectivity control. Assessment of predictability using a dataset of 139 molecules analyzed at different temperatures elucidated the molecular descriptors (MDs) relevant to retention mechanisms. Linear regression, support vector regression (SVR), and tree-based ensemble models were evaluated, with no standout performer. The precision, accuracy, and robustness of models were validated through metrics, such as <i>r</i> and mean absolute error (MAE), and statistical analysis. At <span>(45,^{circ }hbox {C})</span>, logP predominantly influenced retention, akin to reversed-phase columns, while at <span>(5^{circ }hbox {C})</span>, complex interactions with lipophilic and negative MDs, along with specific functional groups, dictated retention. These findings provide deeper insights into TRLC mechanisms, facilitating method development and maximizing column potential.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00873-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141436001","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":"Llamol: a dynamic multi-conditional generative transformer for de novo molecular design","authors":"Niklas Dobberstein,&nbsp;Astrid Maass,&nbsp;Jan Hamaekers","doi":"10.1186/s13321-024-00863-8","DOIUrl":"10.1186/s13321-024-00863-8","url":null,"abstract":"<p>Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transformer (GPT) models. In our efforts to develop such a tool for exploring the organic chemical space in search of potentially electro-active compounds, we present <i>Llamol</i>, a single novel generative transformer model based on the Llama 2 architecture, which was trained on a 12.5M superset of organic compounds drawn from diverse public sources. To allow for a maximum flexibility in usage and robustness in view of potentially incomplete data, we introduce <i>Stochastic Context Learning</i> (SCL) as a new training procedure. We demonstrate that the resulting model adeptly handles single- and multi-conditional organic molecule generation with up to four conditions, yet more are possible. The model generates valid molecular structures in SMILES notation while flexibly incorporating three numerical and/or one token sequence into the generative process, just as requested. The generated compounds are very satisfactory in all scenarios tested. In detail, we showcase the model’s capability to utilize token sequences for conditioning, either individually or in combination with numerical properties, making <i>Llamol</i> a potent tool for de novo molecule design, easily expandable with new properties.</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00863-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141436540","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":"A BERT-based pretraining model for extracting molecular structural information from a SMILES sequence","authors":"Xiaofan Zheng,&nbsp;Yoichi Tomiura","doi":"10.1186/s13321-024-00848-7","DOIUrl":"10.1186/s13321-024-00848-7","url":null,"abstract":"<p>Among the various molecular properties and their combinations, it is a costly process to obtain the desired molecular properties through theory or experiment. Using machine learning to analyze molecular structure features and to predict molecular properties is a potentially efficient alternative for accelerating the prediction of molecular properties. In this study, we analyze molecular properties through the molecular structure from the perspective of machine learning. We use SMILES sequences as inputs to an artificial neural network in extracting molecular structural features and predicting molecular properties. A SMILES sequence comprises symbols representing molecular structures. To address the problem that a SMILES sequence is different from actual molecular structural data, we propose a pretraining model for a SMILES sequence based on the BERT model, which is widely used in natural language processing, such that the model learns to extract the molecular structural information contained in the SMILES sequence. In an experiment, we first pretrain the proposed model with 100,000 SMILES sequences and then use the pretrained model to predict molecular properties on 22 data sets and the odor characteristics of molecules (98 types of odor descriptor). The experimental results show that our proposed pretraining model effectively improves the performance of molecular property prediction</p>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00848-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425535","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":"Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds","authors":"Arnau Comajuncosa-Creus,&nbsp;Aksel Lenes,&nbsp;Miguel Sánchez-Palomino,&nbsp;Dylan Dalton,&nbsp;Patrick Aloy","doi":"10.1186/s13321-024-00867-4","DOIUrl":"10.1186/s13321-024-00867-4","url":null,"abstract":"<div><p>Stereochemistry plays a fundamental role in pharmacology. Here, we systematically investigate the relationship between stereoisomerism and bioactivity on over 1 M compounds, finding that a very significant fraction (~ 40%) of spatial isomer pairs show, to some extent, distinct bioactivities. We then use the 3D representation of these molecules to train a collection of deep neural networks (<i>Signaturizers3D</i>) to generate bioactivity descriptors associated to small molecules, that capture their effects at increasing levels of biological complexity (i.e. from protein targets to clinical outcomes). Further, we assess the ability of the descriptors to distinguish between stereoisomers and to recapitulate their different target binding profiles. Overall, we show how these new stereochemically-aware descriptors provide an even more faithful description of complex small molecule bioactivity properties, capturing key differences in the activity of stereoisomers.</p><p><b>Scientific contribution</b></p><p>We systematically assess the relationship between stereoisomerism and bioactivity on a large scale, focusing on compound-target binding events, and use our findings to train novel deep learning models to generate stereochemically-aware bioactivity signatures for any compound of interest.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00867-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417136","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":"PubChem synonym filtering process using crowdsourcing","authors":"Sunghwan Kim,&nbsp;Bo Yu,&nbsp;Qingliang Li,&nbsp;Evan E. Bolton","doi":"10.1186/s13321-024-00868-3","DOIUrl":"10.1186/s13321-024-00868-3","url":null,"abstract":"<div><p>PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a “chemical synonym”). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem’s crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem’s filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"16 1","pages":""},"PeriodicalIF":8.6,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-024-00868-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141330048","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}