Journal of Cheminformatics最新文献

{"title":"The pucke.rs toolkit to facilitate sampling the conformational space of biomolecular monomers","authors":"Jérôme Rihon,&nbsp;Sten Reynders,&nbsp;Vitor Bernardes Pinheiro,&nbsp;Eveline Lescrinier","doi":"10.1186/s13321-025-00977-7","DOIUrl":"10.1186/s13321-025-00977-7","url":null,"abstract":"<div><p>Understanding of the structural and dynamic behaviour of molecules is a major objective in molecular modeling research. Sampling through the torsional space is an efficient way to map their behaviour. However, generating a landscape of possible conformations relies on multiple formalisms whose mathematics are often difficult to convert to code. Here we present a command line tool and a scripting module to provide the means to generate such landscapes with different axes according to various formalisms exploited for conformational sampling. Additionally to this toolkit, we apply a benchmarking study on subjecting a DNA nucleoside to a diverse set of quantum mechanical levels of theory for geometry optimisations and energy potential calculations. The potential of the tool is demonstrated on examples including amino acids and synthetic nucleosides having five-membered or six-membered sugar moieties.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00977-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841665","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":"Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery","authors":"Maria Zavadskaya,&nbsp;Anastasia Orlova,&nbsp;Andrei Dmitrenko,&nbsp;Vladimir Vinogradov","doi":"10.1186/s13321-025-00998-2","DOIUrl":"10.1186/s13321-025-00998-2","url":null,"abstract":"<div><p>Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and a promising therapeutic target for the management of autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy and safety profiles remain suboptimal, necessitating the exploration of novel compounds. The study introduces a novel deep reinforcement learning strategy for drug discovery, specifically designed to identify new Syk inhibitors. The approach integrates quantitative structure–activity relationship (QSAR) predictions with generative modelling, employing a stacking-ensemble model that achieves a correlation coefficient of 0.78. From over 78,000 molecules generated by this methodology, we identified 139 promising candidates with high predicted potency, binding affinity and optimal drug-likeness properties, demonstrating structural novelty while maintaining essential Syk inhibitor characteristics. Our approach establishes a versatile framework for accelerated drug discovery, which is particularly valuable for the development of rare disease therapeutics.</p><p><b>Scientific contribution</b></p><p>The study presents the first application of QSAR-guided reinforcement learning for Syk inhibitor discovery, yielding structurally novel candidates with predicted high potency. The presented methodology can be adapted for other therapeutic targets, potentially accelerating the drug development process.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00998-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830813","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":"InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem","authors":"Seungchan An,&nbsp;Yeonjin Lee,&nbsp;Junpyo Gong,&nbsp;Seokyoung Hwang,&nbsp;In Guk Park,&nbsp;Jayhyun Cho,&nbsp;Min Ju Lee,&nbsp;Minkyu Kim,&nbsp;Yun Pyo Kang,&nbsp;Minsoo Noh","doi":"10.1186/s13321-025-00999-1","DOIUrl":"10.1186/s13321-025-00999-1","url":null,"abstract":"<div><p>The development of robust artificial intelligence (AI)-driven predictive models relies on high-quality, diverse chemical datasets. However, the scarcity of negative data and a publication bias toward positive results often hinder accurate biological activity prediction. To address this challenge, we introduce InertDB, a comprehensive database comprising 3,205 curated inactive compounds (CICs) identified through rigorous review of over 4.6 million compound records in PubChem. CIC selection prioritized bioassay diversity, determined using natural language processing (NLP)-based clustering metrics, while ensuring minimal biological activity across all evaluated bioassays. Notably, 97.2% of CICs adhere to the Rule of Five, a proportion significantly higher than that of overall PubChem dataset. To further expand the chemical space, InertDB also features 64,368 generated inactive compounds (GICs) produced using a deep generative AI model trained on the CIC dataset. Compared to conventional approaches such as random sampling or property-matched decoys, InertDB significantly improves predictive AI performance, particularly for phenotypic activity prediction by providing reliable inactive compound sets.</p><p><b>Scientific contributions</b></p><p>InertDB addresses a critical gap in AI-driven drug discovery by providing a comprehensive repository of biologically inactive compounds, effectively resolving the scarcity of negative data that limits prediction accuracy and model reliability. By leveraging language model-based bioassay diversity metrics and generative AI, InertDB integrates rigorously curated inactive compounds with an expanded chemical space. InertDB serves as a valuable alternative to random sampling and decoy generation, offering improved training datasets and enhancing the accuracy of phenotypic pharmacological activity prediction.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00999-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809238","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":"Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop","authors":"Youngchun Kwon,&nbsp;Hyunjeong Jeon,&nbsp;Joonhyuk Choi,&nbsp;Youn-Suk Choi,&nbsp;Seokho Kang","doi":"10.1186/s13321-025-00987-5","DOIUrl":"10.1186/s13321-025-00987-5","url":null,"abstract":"<div><p>In synthesis planning, identifying and optimizing chemical reactions are important for the successful design of synthetic pathways to target substances. Chemical reaction databases assist chemists in gaining insights into this process. Traditionally, searching for relevant records from a reaction database has relied on the manual formulation of queries by chemists based on their search purposes, which is challenging without explicit knowledge of what they are searching for. In this study, we propose an intelligent chemical reaction search system that simplifies the process of enhancing the search results. When a user submits a query, a list of relevant records is retrieved from the reaction database. Users can express their preferences and requirements by providing binary ratings for the individual retrieved records. The search results are refined based on the user feedback. To implement this system effectively, we incorporate and adapt contrastive representation learning, dimensionality reduction, and human-in-the-loop techniques. Contrastive learning is used to train a representation model that embeds records in the reaction database as numerical vectors suitable for chemical reaction searches. Dimensionality reduction is applied to compress these vectors, thereby enhancing the search efficiency. Human-in-the-loop is integrated to iteratively update the representation model by reflecting user feedback. Through experimental investigations, we demonstrate that the proposed method effectively improves the chemical reaction search towards better alignment with user preferences and requirements. </p><p><b>Scientific contribution</b> This study seeks to enhance the search functionality of chemical reaction databases by drawing inspiration from recommender systems. The proposed method simplifies the search process, offering an alternative to the complexity of formulating explicit query rules. We believe that the proposed method can assist users in efficiently discovering records relevant to target reactions, especially when they encounter difficulties in crafting detailed queries due to limited knowledge.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00987-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809358","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":"Unveiling polyphenol-protein interactions: a comprehensive computational analysis","authors":"Samo Lešnik,&nbsp;Marko Jukić,&nbsp;Urban Bren","doi":"10.1186/s13321-025-00997-3","DOIUrl":"10.1186/s13321-025-00997-3","url":null,"abstract":"<div><p>Our study investigates polyphenol-protein interactions, analyzing their structural diversity and dynamic behavior. Analysis of the entire Protein Data Bank reveals diverse polyphenolic structures, engaging in various noncovalent interactions with proteins. Interactions observed across crystal structures among diverse polyphenolic classes reveal similarities, underscoring consistent patterns across a spectrum of structural motifs. On the other hand, molecular dynamics (MD) simulations of polyphenol-protein complexes unveil dynamic binding patterns, highlighting the influx of water molecules into the binding site and underscoring limitations of static crystal structures. Water-mediated interactions emerge as crucial in polyphenol-protein binding, leading to variable binding patterns observed in MD simulations. Comparison of high- and low-resolution crystal structures as starting points for MD simulations demonstrates their robustness, exhibiting consistent dynamics regardless of the quality of the initial structural data. Additionally, the impact of glycosylation on polyphenol binding is explored, revealing its role in modulating interactions with proteins. In contrast to synthetic drugs, polyphenol binding seems to exhibit heightened flexibility, driven by dynamic water-mediated interactions, which may also facilitate their promiscuous binding. Comprehensive dynamic studies are, therefore essential to understand polyphenol-protein recognition mechanisms. Overall, our study provides novel insights into polyphenol-protein interactions, informing future research for harnessing polyphenolic therapeutic potential through rational drug design.</p><p><b>Scientific contribution</b>: In this study, we present an analysis of (natural) polyphenol-protein binding conformations, leveraging the entirety of the Protein Data Bank structural data on polyphenols, while extending the binding conformation sampling through molecular dynamics simulations. For the first time, we introduce experimentally supported large-scale systematization of polyphenol binding patterns. Moreover, our insight into the significance of explicit water molecules and hydrogen-bond bridging rationalizes the polyphenol promiscuity paradigm, advocating for a deeper understanding of polyphenol recognition mechanisms crucial for informed natural compound-based drug design.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00997-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809239","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 beginner’s approach to deep learning applied to VS and MD techniques","authors":"Stijn D’Hondt,&nbsp;José Oramas,&nbsp;Hans De Winter","doi":"10.1186/s13321-025-00985-7","DOIUrl":"10.1186/s13321-025-00985-7","url":null,"abstract":"<div><p>It has become impossible to imagine the fields of biochemistry and medicinal chemistry without computational chemistry and molecular modelling techniques. In many steps of the drug development process in silico methods have become indispensable. Virtual screening (VS) can tremendously expedite the early discovery phase, whilst the use of molecular dynamics (MD) simulations forms a powerful additional tool to in vitro methods throughout the entire drug discovery process. In the field of biochemistry, MD has also become a compelling method for studying biophysical systems (e.g., protein folding) complementary to experimental techniques. However, both VS and MD come with their own limitations and methodological difficulties, from hardware limitations to restrictions in algorithmic capabilities. One solution to overcoming these difficulties lies in the field of machine learning (ML), and more specifically deep learning (DL). There are many ways in which DL can be applied to these molecular modelling techniques to achieve more accurate results in a more efficient manner or expedite the data analysis of the acquired results. Despite steadily increasing interest in DL amidst computational chemists, knowledge is still limited and scattered over different resources. This review is aimed at computational chemists with knowledge of molecular modelling, who wish to possibly integrate DL approaches in their research and already have a basic understanding of the fundamentals of DL. This review focusses on a survey of recent applications of DL in molecular modelling techniques. The different sections are logically subdivided, based on where DL is integrated in the research: (1) for the improvement of VS workflows, (2) for the improvement of certain workflows in MD simulations, (3) for aiding in the calculations of interatomic forces, or (4) for data analysis of MD trajectories. It will become clear that DL has the capacity to completely transform the way molecular modelling is carried out.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00985-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793265","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":"HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model","authors":"Jiyeon Han,&nbsp;Wonho Zhung,&nbsp;Insoo Jang,&nbsp;Joongwon Lee,&nbsp;Min Ji Kang,&nbsp;Timothy Dain Lee,&nbsp;Seung Jun Kwack,&nbsp;Kyu-Bong Kim,&nbsp;Daehee Hwang,&nbsp;Byungwook Lee,&nbsp;Hyung Sik Kim,&nbsp;Woo Youn Kim,&nbsp;Sanghyuk Lee","doi":"10.1186/s13321-025-00992-8","DOIUrl":"10.1186/s13321-025-00992-8","url":null,"abstract":"<div><p>Liver toxicity poses a critical challenge in drug development due to the liver's pivotal role in drug metabolism and detoxification. Accurately predicting liver toxicity is crucial but is hindered by scattered information sources, a lack of curation standards, and the heterogeneity of data perspectives. To address these challenges, we developed the HepatoToxicity Portal (HTP), which integrates an expert-curated knowledgebase (HTP-KB) and a state-of-the-art machine learning model for toxicity prediction (HTP-Pred). The HTP-KB consolidates hepatotoxicity data from nine major databases, carefully reviewed by hepatotoxicity experts and categorized into three levels: in vitro, in vivo, and clinical, using the Medical Dictionary for Regulatory Activities (MedDRA) terminology. The knowledgebase includes information on 8,306 chemicals. This curated dataset was used to build a hepatotoxicity prediction module by fine-tuning a GNN-based foundation model, which was pre-trained with approximately 10 million chemicals in the PubChem database. Our model demonstrated excellent performance, achieving an area under the ROC curve (AUROC) of 0.761, surpassing existing methods for hepatotoxicity prediction. The HTP is publicly accessible at https://kobic.re.kr/htp/, offering both curated data and prediction services through an intuitive interface, thus effectively supporting drug development efforts.</p><p><b>Scientific contributions</b></p><p>HTP-KB consolidates comprehensive curated information on liver toxicity gathered from nine sources. HTP-Pred utilizes advanced deep learning techniques, significantly enhancing predictive accuracy. Together, these tools provide valuable resources for researchers and practitioners in drug development, accessible through a user-friendly interface.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00992-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143798054","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":"AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities?","authors":"Rachelle J. Bienstock","doi":"10.1186/s13321-025-00995-5","DOIUrl":"10.1186/s13321-025-00995-5","url":null,"abstract":"<div><p>Cheminformatics and chemical databases are essential to drug discovery. However, machine learning (ML) and artificial intelligence (AI) methodologies are changing the way in which chemical data is used. How will the use of chemical data change in drug discovery moving forward? How do the new ML methods in molecular property prediction, hit and lead and target identification and structure prediction differ and compare with previous computational methods? Will new ML methodologies improve chemical diversity in ligand design, and offer computational enhancements. There are still many advantages to physics based methods and they offer something lacking in ML/ AI based methods. Additionally, ML training methods often give the best results when experimental assay measurements are fed back into the model. Often modeling and experimental methods are not diametrically opposed but offer the greatest advantage when used complementary.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00995-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786622","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 evolution of open science in cheminformatics: a journey from closed systems to collaborative innovation","authors":"Christoph Steinbeck","doi":"10.1186/s13321-025-00990-w","DOIUrl":"10.1186/s13321-025-00990-w","url":null,"abstract":"<div><p>Cheminformatics has significantly transformed over the past four decades, evolving from a field dominated by proprietary systems to one increasingly embracing open science principles. In its early years, cheminformatics was characterised by commercial software and restricted data access, limiting collaboration and reproducibility. The advent of open-source software in the late 1990s and early 2000s, including tools such as the Chemistry Development Kit (CDK) and RDKit, played a crucial role in democratising computational chemistry. Open data initiatives, such as PubChem and NMRShiftDB, further enhanced accessibility by providing freely available chemical information, fostering transparency and interoperability and introducing key standards, such as the International Chemical Identifier (InChI), revolutionised data integration and retrieval across diverse platforms. Community-driven efforts, including the Blue Obelisk movement and Open Notebook Science, have promoted open methodologies and collaborative research. More recently, national data infrastructure projects like NFDI4Chem have aimed to standardise research data management in cheminformatics, ensuring the long-term sustainability of open science practices. The increasing adoption of the FAIR (Findable, Accessible, Interoperable, Reusable) principles has further reinforced data sharing and reuse in computational chemistry. Challenges remain, particularly in overcoming resistance to data sharing and ensuring sustainable funding for open projects. However, the trajectory of cheminformatics demonstrates that embracing openness enhances scientific integrity and accelerates discovery and innovation.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00990-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766416","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":"Clc-db: an open-source online database of chiral ligands and catalysts","authors":"Gufeng Yu,&nbsp;Kaiwen Yu,&nbsp;Xi Wang,&nbsp;Chenxi Zhang,&nbsp;Yicong Luo,&nbsp;Xiaohong Huo,&nbsp;Yang Yang","doi":"10.1186/s13321-025-00991-9","DOIUrl":"10.1186/s13321-025-00991-9","url":null,"abstract":"<div><p>The design and optimization of chiral ligands and catalysts are fundamental to advancing asymmetric catalysis, a critical area in organic chemistry with wide-ranging impacts across scientific disciplines. Traditional experimental approaches, while essential, are often hindered by their slow pace and complexity. Recent advancements have demonstrated that computational methods, particularly machine learning, offer transformative potential by significantly accelerating these processes through enhanced prediction and modeling capabilities. However, limitations such as data scarcity and model inaccuracies continue to challenge their broader application. To address these issues, we present the Chiral Ligand and Catalyst Database (CLC-DB), the first open-source, comprehensive database specifically designed for chiral ligands and catalysts. CLC-DB contains 1,861 molecules spanning 32 distinctive chiral ligand and catalyst categories, with each entry annotated with 34 types of curated information, validated by chemical experts and linked to authoritative chemical databases. The database features a user-friendly interface that supports efficient single and batch searches, as well as an integrated, high-performance online molecular clustering tool to facilitate computational analyses. CLC-DB is freely accessible at https://compbio.sjtu.edu.cn/services/clc-db, where all data are available for download.</p></div>","PeriodicalId":617,"journal":{"name":"Journal of Cheminformatics","volume":"17 1","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://jcheminf.biomedcentral.com/counter/pdf/10.1186/s13321-025-00991-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766415","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}