Artificial intelligence chemistry最新文献_第2页

User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals 面向药物化学家和制药业的用户友好型工业集成人工智能

Artificial intelligence chemistry Pub Date : 2024-07-14 DOI: 10.1016/j.aichem.2024.100072

Olga Kapustina , Polina Burmakina , Nina Gubina , Nikita Serov , Vladimir Vinogradov

{"title":"User-friendly and industry-integrated AI for medicinal chemists and pharmaceuticals","authors":"Olga Kapustina , Polina Burmakina , Nina Gubina , Nikita Serov , Vladimir Vinogradov","doi":"10.1016/j.aichem.2024.100072","DOIUrl":"10.1016/j.aichem.2024.100072","url":null,"abstract":"<div><p>Artificial intelligence has brought crucial changes to the whole field of natural sciences. Myriads of machine learning algorithms have been developed to facilitate the work of experimental scientists. Molecular property prediction and drug synthesis planning become routine tasks. Moreover, inverse design of compounds with tunable properties as well as on-the-fly autonomous process optimization and chemical space exploration became possible <em>in silico</em>. Affordable robotic platforms exist able to perform thousands of experiments every day, analyzing the results and tuning the protocols. Despite this, most of these developments get trapped at the stage of code or overlooked, limiting their use by experimental scientists. Meanwhile, visibility and the number of user-friendly tools and technologies available to date is too low to compensate for this fact, rendering the development of novel therapeutic compounds inefficient. In this Review, we set the goal to bridge the gap between modern technologies and experimental scientists to improve drug development efficacy. Here we survey advanced and easy-to-use technologies able to help medical chemists at every stage of their research, including those integrated in technological processes during COVID-19 pandemic motivated by the need for fast yet precise solutions. Moreover, we review how these technologies are integrated by industry and clinics to streamline drug development and production. These technologies already transform the current paradigm of scientific thinking and revolutionize not only medicinal chemistry, but the whole field of natural sciences.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100072"},"PeriodicalIF":0.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000307/pdfft?md5=6e0dd6833ec368337f7792d55171a0b8&pid=1-s2.0-S2949747724000307-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141691742","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 comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds 利用量子机器学习研究喹喔啉化合物缓蚀作用的综合方法

Artificial intelligence chemistry Pub Date : 2024-07-10 DOI: 10.1016/j.aichem.2024.100073

Muhamad Akrom , Supriadi Rustad , Hermawan Kresno Dipojono , Ryo Maezono

{"title":"A comprehensive approach utilizing quantum machine learning in the study of corrosion inhibition on quinoxaline compounds","authors":"Muhamad Akrom , Supriadi Rustad , Hermawan Kresno Dipojono , Ryo Maezono","doi":"10.1016/j.aichem.2024.100073","DOIUrl":"10.1016/j.aichem.2024.100073","url":null,"abstract":"<div><p>In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R<sup>2</sup>) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid <strong>(Q1)</strong>, methyl quinoxaline-6-carboxylate <strong>(Q2)</strong>, (2<em>E</em>,3<em>E</em>)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline <strong>(Q3)</strong>, (2<em>E</em>,3<em>E</em>) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline <strong>(Q4)</strong>, (<em>E</em>)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one <strong>(Q5)</strong>, and 2-(4-methoxyphenyl)-7-methylthieno[3,2-<em>b</em>] quinoxaline <strong>(Q6)</strong>, show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000319/pdfft?md5=a7bac287a83d3748d5afc1de5b5f817c&pid=1-s2.0-S2949747724000319-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141630521","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

Artificial intelligence for drug repurposing against infectious diseases 人工智能为防治传染病重新设计药物用途

Artificial intelligence chemistry Pub Date : 2024-06-12 DOI: 10.1016/j.aichem.2024.100071

Anuradha Singh

{"title":"Artificial intelligence for drug repurposing against infectious diseases","authors":"Anuradha Singh","doi":"10.1016/j.aichem.2024.100071","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100071","url":null,"abstract":"<div><p>Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 2","pages":"Article 100071"},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000290/pdfft?md5=061eb4d8766a2284afb22976a6d28b51&pid=1-s2.0-S2949747724000290-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141322997","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

Drug discovery and development in the era of artificial intelligence: From machine learning to large language models 人工智能时代的药物发现与开发：从机器学习到大型语言模型

Artificial intelligence chemistry Pub Date : 2024-05-09 DOI: 10.1016/j.aichem.2024.100070

Shenghui Guan , Guanyu Wang

{"title":"Drug discovery and development in the era of artificial intelligence: From machine learning to large language models","authors":"Shenghui Guan , Guanyu Wang","doi":"10.1016/j.aichem.2024.100070","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100070","url":null,"abstract":"<div><p>Drug Research and Development (R&D) is a complex and difficult process, and current drug R&D faces the challenges of long time span, high investment, and high failure rate. Machine learning, with its powerful learning ability to characterize big data and complex networks, is increasingly effective to improve the efficiency and success rate of drug R&D. Here we review some recent examples of the application of machine learning methods in six areas: disease gene prediction, virtual screening, drug molecule generation, molecular attribute prediction, and prediction of drug combination synergism. We also discuss the advantages of integrative learning in multi-attribute prediction. Integrative models based on base learners constructed from data of different dimensions on the one hand fully utilize the information contained in these data, and on the other hand improve the average prediction performance. Finally, we envision a new paradigm for drug discovery and development: a large language model acts as a central hub to organize public resources into a knowledge base, validating the knowledge with computational software and smaller predictive models, as well as high-throughput automated screening platforms based on organoidal technologies, to speed up development and reduce the differences in efficacy between disease models and humans to improve the success rate of a drug.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100070"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000289/pdfft?md5=3f360afc13a60f15f7dab8b6a1dd740b&pid=1-s2.0-S2949747724000289-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906077","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

Spatial-temporal self-attention network based on bayesian optimization for light olefins yields prediction in methanol-to-olefins process 基于贝叶斯优化的时空自我关注网络，用于预测甲醇制烯烃过程中的轻质烯烃产量

Artificial intelligence chemistry Pub Date : 2024-04-30 DOI: 10.1016/j.aichem.2024.100067

Jibin Zhou , Duiping Liu , Mao Ye , Zhongmin Liu

{"title":"Spatial-temporal self-attention network based on bayesian optimization for light olefins yields prediction in methanol-to-olefins process","authors":"Jibin Zhou , Duiping Liu , Mao Ye , Zhongmin Liu","doi":"10.1016/j.aichem.2024.100067","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100067","url":null,"abstract":"<div><p>Methanol-to-olefins (MTO), as an alternative pathway for the synthesis of light olefins (ethylene and propylene), has gained extensive attention. Accurate prediction of light olefins yields can effectively facilitate process monitoring and optimization, as they are significant economic indexes and stable operation indicators of the industrial MTO process. However, the nonlinearity and dynamic interactions among process variables pose challenges for the prediction using traditional statistical methods. Additionally, physical-based methods relying on first-principle theory are always limited by an insufficient understanding of reaction mechanisms. In contrast, data-driven methods offer a viable solution for the prediction based solely on process data without requiring extensive process knowledge. Therefore, in this work, a data-driven approach that integrates spatial and temporal self-attention modules is proposed to capture complex interactions. Furthermore, Bayesian optimization is employed to determine the optimum hyperparameters and enhance the accuracy of the model. Studies on an actual MTO process demonstrate the superior prediction performance of the proposed model compared to baseline models. Specifically, 24 process variables are selected as the high-dimensional inputs, and yields of ethylene and propylene, as the low-dimensional outputs, are successfully predicted at various prediction horizons ranging from 2 to 8 h.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100067"},"PeriodicalIF":0.0,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000253/pdfft?md5=23aede3f145af7617d071f10a10c1e3f&pid=1-s2.0-S2949747724000253-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880534","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

Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions 通过机器学习确定二元溶液中水的活度和电解质的活度系数

Artificial intelligence chemistry Pub Date : 2024-04-27 DOI: 10.1016/j.aichem.2024.100069

Guillaume Zante

{"title":"Machine learning for determination of activity of water and activity coefficients of electrolytes in binary solutions","authors":"Guillaume Zante","doi":"10.1016/j.aichem.2024.100069","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100069","url":null,"abstract":"<div><p>Activity of water and electrolytes in aqueous solutions is of utmost importance for multiple industrial applications. However, experimental determination of such values is time-consuming, while calculation of activity coefficients using numerical methods is challenging. By training neural networks models on literature data, one could predict activity of water and electrolytes easily, without requiring any experiment. In this paper, multiple descriptors (or features) are compared to predict activity coefficients of electrolytes and activity of water in electrolyte solutions. A neural network based on the Levenberg-Marquardt algorithm (LM-NN) showed high accuracy to calculate values, despite the small size of the training datasets. Both activity coefficients of electrolytes and activity of water in electrolyte solutions can be predicted accurately even on unseen data, using simple descriptors such as electrolyte concentration, ion sizes and charges. However, some discrepancies were observed due to the lack of representativeness of the training dataset. This could be solved by selecting training data sets that are similar (<em>e.g.</em> same group of the periodic table) to the unknown values, or by including available experimental data for the salt considered. The ability of the LM-NN to solve non-linear least square curve fitting problems makes it a good candidate to fit experimental activity coefficient data, with the advantage of simplicity as compared to e-NRTL or UNIQUAC methods. This method paves the way for accurate and quick determination of thermodynamic data for electrolyte solutions (and beyond) using machine learning, without necessitating large training datasets.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100069"},"PeriodicalIF":0.0,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000277/pdfft?md5=e459d6b44dc65480007e2791c28b76ea&pid=1-s2.0-S2949747724000277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140825119","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

Recent advances of machine learning applications in the development of experimental homogeneous catalysis 机器学习应用于均相催化实验开发的最新进展

Artificial intelligence chemistry Pub Date : 2024-04-27 DOI: 10.1016/j.aichem.2024.100068

Nil Sanosa , David Dalmau , Diego Sampedro , Juan V. Alegre-Requena , Ignacio Funes-Ardoiz

引用次数: 0

Automated kinetics measurement for homogeneous photocatalytic reactions in continuous microflow 连续微流中均相光催化反应的自动动力学测量

Artificial intelligence chemistry Pub Date : 2024-04-21 DOI: 10.1016/j.aichem.2024.100066

Yujie Wang , Jian Li , Xuze Chen , Weiping Zhu , Xuhong Guo , Fang Zhao

{"title":"Automated kinetics measurement for homogeneous photocatalytic reactions in continuous microflow","authors":"Yujie Wang , Jian Li , Xuze Chen , Weiping Zhu , Xuhong Guo , Fang Zhao","doi":"10.1016/j.aichem.2024.100066","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100066","url":null,"abstract":"<div><p>Photocatalytic reactions, achieving chemical synthesis in a more sustainable manner than thermal reactions, have been demonstrated to become more efficient, greener and easier to scale up when combined with continuous microflow technology. Nevertheless, the report on the kinetics measurement for photocatalytic reactions in continuous microflow, especially in a fully automated way, is very rare. In this work, two challenging parameters, i.e., the reaction order with respect to oxygen (2.48) and photoreaction activation energy (-16.83 kJ/mol) of the photocatalytic oxidation of 9,10-diphenylanthracene, were acquired in an automated continuous flow platform using the Steady-state Method. Moreover, the Ramping Method was also successfully implemented in the automated continuous flow photoreaction platform, exhibiting a predictive accuracy of 4.42 %, with 64.3 % less time and 58.0 % less material consumption than the Steady-state Method. And it was found that the improvement in the residence time distribution of the microreactor could improve the accuracy of the Ramping Method. The automated continuous flow process developed in this work could offer an efficient and accurate way to attain the reaction kinetics information for homogeneous photocatalytic reactions.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2024-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000241/pdfft?md5=acff6c610e496d29876c4bc9832b2989&pid=1-s2.0-S2949747724000241-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140644835","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

Data mining of stable, low-cost metal oxides as potential electrocatalysts 作为潜在电催化剂的稳定、低成本金属氧化物的数据挖掘

Artificial intelligence chemistry Pub Date : 2024-04-16 DOI: 10.1016/j.aichem.2024.100065

Xue Jia, Hao Li

{"title":"Data mining of stable, low-cost metal oxides as potential electrocatalysts","authors":"Xue Jia, Hao Li","doi":"10.1016/j.aichem.2024.100065","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100065","url":null,"abstract":"<div><p>Metal oxides (MOs) are a class of electrocatalysts which could be the low-cost alternatives to precious metals. However, many MOs suffer from poor stability under electrochemical operating conditions. The <em>Materials Project</em> stands out as one of the largest computational materials databases to date, where the bulk Pourbaix diagrams are essential in assessing the aqueous stability of potential electrocatalysts. Herein, we performed data mining from the <em>Materials Project</em> database to identify potentially stable MOs for industrially important electrocatalytic reactions including oxygen reduction reaction (ORR), oxygen evolution reaction (OER), chlorine evolution reaction (CER), hydrogen evolution reaction (HER), and nitrogen reduction reaction (NRR). We found that many MOs can be potentially stable under electrocatalytic conditions, especially in neutral and alkaline medium. Finally, we summarized those MOs that had been previously experimentally synthesized but haven’t been explored as electrocatalysts. This comprehensive assessment effectively narrows down the exploration scope and facilitates the evaluation of material stability.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100065"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294974772400023X/pdfft?md5=b8129e021daa42ae50521062f842ed41&pid=1-s2.0-S294974772400023X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140645362","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

RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes RedPred：用于预测水有机电解质氧化还原反应能量的机器学习模型

Artificial intelligence chemistry Pub Date : 2024-04-04 DOI: 10.1016/j.aichem.2024.100064

Murat Cihan Sorkun , Elham Nour Ghassemi , Cihan Yatbaz , J.M. Vianney A. Koelman , Süleyman Er

{"title":"RedPred, a machine learning model for the prediction of redox reaction energies of the aqueous organic electrolytes","authors":"Murat Cihan Sorkun , Elham Nour Ghassemi , Cihan Yatbaz , J.M. Vianney A. Koelman , Süleyman Er","doi":"10.1016/j.aichem.2024.100064","DOIUrl":"https://doi.org/10.1016/j.aichem.2024.100064","url":null,"abstract":"<div><p>Aqueous Organic Redox Flow Batteries (AORFBs) are considered as one of the most appealing technologies for large-scale energy storage due to their electroactive organic materials, which are abundant, easy to produce, and recyclable. A prevailing challenge for the redox chemistries applied in AORFBs is to achieve high power and energy density. The chemical design and molecular engineering of the electroactive compounds is an effective approach for the optimization of their physicochemical properties. Among them, the reaction energy of redox couples is often used as a proxy for the measured potentials. In this study, we present RedPred, a machine learning (ML) model that predicts the one-step two-electron two-proton redox reaction energy of redox-active molecule pairs. RedPred comprises an ensemble of Artificial Neural Networks, Random Forests, and Graph Convolutional Networks, trained using the RedDB database, which contains over 15,000 reactant-product pairs for AORFBs. We evaluated RedPred’s performance using six different molecular encoders and five prominent ML algorithms applied in chemical science. The predictive capability of RedPred was tested on both its training chemical space and the chemical space outside its training domain using two separate test datasets. We released a user-friendly web tool with open-source code to promote software sustainability and broad use.</p></div>","PeriodicalId":72302,"journal":{"name":"Artificial intelligence chemistry","volume":"2 1","pages":"Article 100064"},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949747724000228/pdfft?md5=eb00d9969ca643436121b54e6f5cf72b&pid=1-s2.0-S2949747724000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140539637","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