Journal of Chemical Information and Modeling 最新文献

{"title":"BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>.","authors":"Jianxiu Cai, Jielu Yan, Chonwai Un, Yapeng Wang, François-Xavier Campbell-Valois, Shirley W I Siu","doi":"10.1021/acs.jcim.4c01749","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01749","url":null,"abstract":"<p><p>Antimicrobial peptides (AMPs) are a promising alternative for combating bacterial drug resistance. While current computer prediction models excel at binary classification of AMPs based on sequences, there is a lack of regression methods to accurately quantify AMP activity against specific bacteria, making the identification of highly potent AMPs a challenge. Here, we present a deep learning method, BERT-AmPEP60, based on the fine-tuned Bidirectional Encoder Representations from Transformers (BERT) architecture to extract embedding features from input sequences. Using the transfer learning strategy, we built regression models to predict the minimum inhibitory concentration (MIC) of peptides for <i>Escherichia coli</i> (EC) and <i>Staphylococcus aureus</i> (SA). In five independent experiments with 10% leave-out sequences as the test sets, the optimal EC and SA models outperformed the state-of-the-art regression method and traditional machine learning methods, achieving an average mean squared error of 0.2664 and 0.3032 (log μM), respectively. They also showed a Pearson correlation coefficient of 0.7955 and 0.7530, and a Kendall correlation coefficient of 0.5797 and 0.5222, respectively. Our models outperformed existing deep learning and machine learning methods that rely on conventional sequence features. This work underscores the effectiveness of utilizing BERT with transfer learning for training quantitative AMP prediction models specific for different bacterial species. The web server of BERT-AmPEP60 can be found at https://app.cbbio.online/ampep/home. To facilitate development, the program source codes are available at https://github.com/janecai0714/AMP_regression_EC_SA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629979","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":"Deep-Learning Potential Molecular Dynamics Study on Nanopolycrystalline Al-Er Alloys: Effects of Er Concentration, Grain Boundary Segregation, and Grain Size on Plastic Deformation.","authors":"Zhen Chang, Li Feng, Hong-Tao Xue, Yan-Hong Yang, Jun-Qiang Ren, Fu-Ling Tang, Xue-Feng Lu, Jun-Chen Li","doi":"10.1021/acs.jcim.5c00008","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00008","url":null,"abstract":"<p><p>Understanding the tensile mechanical properties of Al-Er alloys at the atomic scale is essential, and molecular dynamics (MD) simulations offer valuable insights. However, these simulations are constrained by the unavailability of suitable interatomic potentials. In this study, the deep potential (DP) approach, aided by high-throughput first-principles calculations, was utilized to develop an Al-Er interatomic potential specifically for MD simulations. Systematic comparisons between the physical properties (e.g., energy-volume curves, melting point, elastic constants) predicted by the DP model and those obtained from density functional theory (DFT) demonstrated that the developed DP model for Al-Er alloys possesses reliable predictive capabilities while retaining DFT-level accuracy. Our findings confirm that Al₃Er, Al₂Er, and AlEr₂ exhibit mechanical stability. The calculated melting point of Al₃Er (1398 K) shows a 57 K deviation from the experimental value (1341 K). With the Er content increasing from 0.01% to 0.064 at.% in Al-Er alloys, the grain boundary (GB) concentration of Er atoms increases from 0.03 to 0.07% following Monte Carlo (MC) annealing optimization. The Al-0.05 at.%Er alloy exhibits the highest yield strength, with an increase of 0.128 GPa (6.1%) compared to pure Al. For Al-0.05 at.%Er alloys with varying average grain sizes, the GB concentration of Er atoms increases by about 1.4-1.6 times after MC annealing compared to the average Er content. Additionally, the Al-Er alloys reach the peak yield strength of 2.214 GPa when the average grain size is 11.72 nm. The GB segregation of Er atoms lowers the system energy and thus enhances stability. Notable changes in the segregation behavior of Er atoms were observed with increasing Er concentration and decreasing grain size. These results would facilitate the understanding of the mechanical characteristics of Al-Er alloys and offer a theoretical basis for developing advanced nanopolycrystalline Al-Er alloys.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629980","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":"Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein–Ligand Binding Affinity Prediction","authors":"Yaxing Wang,&nbsp;Xiang Liu,&nbsp;Yipeng Zhang,&nbsp;Xiangjun Wang and Kelin Xia*,&nbsp;","doi":"10.1021/acs.jcim.4c0230910.1021/acs.jcim.4c02309","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02309https://doi.org/10.1021/acs.jcim.4c02309","url":null,"abstract":"<p >With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has been shown by topological data analysis (TDA) and TDA-based machine learning models in various steps of drug design and discovery. Here, we propose, for the first time, join persistent homology (JPH) and JPH-based machine learning models for metalloprotein–ligand binding affinity prediction. Mathematically, dramatically different from persistent homology and extended persistent homology, our JPH employs a set of filtration functions to generate a multistage filtration for the join of the original simplicial complex and a specially designed test simplicial complex. From the featurization perspective, our JPH-based molecular descriptors can provide a more comprehensive characterization of the intrinsic topological information of the data. Our JPH descriptors are combined with the gradient boosting tree (GBT) model for metalloprotein–ligand binding affinity prediction. The benchmark dataset for metalloprotein–ligand complexes from PDBbind-v2020 is employed for the validation and comparison of our model. It has been found that our JPH-GBT model can outperform all of the existing models, as far as we know. This demonstrates the great potential of our join persistent homology in the characterization of molecular structures and functions.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"2785–2793 2785–2793"},"PeriodicalIF":5.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675694","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":"FDPSM: Feature-Driven Prediction Modeling of Pathogenic Synonymous Mutations","authors":"Fangfang Jin,&nbsp;Na Cheng,&nbsp;Lihua Wang,&nbsp;Bin Ye* and Junfeng Xia*,&nbsp;","doi":"10.1021/acs.jcim.4c0213910.1021/acs.jcim.4c02139","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02139https://doi.org/10.1021/acs.jcim.4c02139","url":null,"abstract":"<p >Synonymous mutations, once considered to be biologically neutral, are now recognized to affect protein expression and function by altering the RNA splicing, stability, or translation efficiency. These effects can contribute to disease, making the prediction of the pathogenicity a crucial task. Computational methods have been developed to analyze the sequence features and biological functions of synonymous mutations, but existing methods face limitations, including scarcity of labeled data, reliance on other prediction tools, and insufficient representation of feature interrelationships. Here, we present FDPSM, a novel prediction method specifically designed to predict pathogenic synonymous mutations. FDPSM was trained on a robust data set of 4251 positive and negative training samples to enhance predictive accuracy. The method leveraged a comprehensive set of features, including genomic context, conservation, splicing effects, functional effects, and epigenomics, without relying on prediction scores from other mutation pathogenicity tools. Recognizing that original features alone may not fully capture the distinctions between pathogenic and benign synonymous mutations, we enhanced the feature set by extracting effective information from the interactions and distribution of these features. The experimental results showed that FDPSM significantly outperformed existing methods in predicting the pathogenicity of synonymous mutations, offering a more accurate and reliable tool for this important task. FDPSM is available at https://github.com/xialab-ahu/FDPSM.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"3064–3076 3064–3076"},"PeriodicalIF":5.6,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675697","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":"Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning","authors":"Yanglan Gan*,&nbsp;Shengnan Li,&nbsp;Guangwei Xu,&nbsp;Cairong Yan and Guobing Zou,&nbsp;","doi":"10.1021/acs.jcim.4c0242410.1021/acs.jcim.4c02424","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02424https://doi.org/10.1021/acs.jcim.4c02424","url":null,"abstract":"<p >The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced to predict possible drug–disease relationships. Nevertheless, current graph-based methods tend to model drug–disease interaction relationships without considering the semantic influence of node-specific side information on graphs. These approaches also suffer from the noise and sparsity inherent in the data. To address these limitations, we propose MDGCN, a novel drug repositioning method that incorporates multidependency graph convolutional networks and contrastive learning. Based on drug and disease similarity matrices and the drug–disease relationships matrix, this approach constructs multidependency graphs. It subsequently employs graph convolutional networks to spread side information between various graphs in each layer. Meanwhile, the weak supervision of drug–disease connections is effectively addressed by introducing cross-view and cross-layer contrastive learning to align node embedding across various views. Extensive experiments show that MDGCN performs better in drug–disease association prediction than seven advanced methods, offering strong support for investigating novel therapeutic indications for medications of interest.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"3090–3103 3090–3103"},"PeriodicalIF":5.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675837","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":"Fluor-Predictor: An Interpretable Tool for Multiproperty Prediction and Retrieval of Fluorescent Dyes","authors":"Wenxiang Song,&nbsp;Le Xiong,&nbsp;Xinmin Li,&nbsp;Yuyang Zhang,&nbsp;Binya Wang,&nbsp;Guixia Liu,&nbsp;Weihua Li,&nbsp;Youjun Yang* and Yun Tang*,&nbsp;","doi":"10.1021/acs.jcim.5c0012710.1021/acs.jcim.5c00127","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00127https://doi.org/10.1021/acs.jcim.5c00127","url":null,"abstract":"<p >With the rapid advancements in the field of fluorescent dyes, accurate prediction of optical properties and efficient retrieval of dye-related data are essential for effective dye design. However, there is a lack of tools for comprehensive data integration and convenient data retrieval. Moreover, existing prediction models mainly focus on a single property of fluorescent dyes and fail to account for the diverse fluorophores and solutions in a systematic manner. To address this, we proposed Fluor-predictor, a multitask prediction model for fluorophores. This study integrates multiple dye databases and develops an interpretable graph neural network-based multitask regression model to predict four key optical properties of fluorescent dyes. We thoroughly examined the impact of factors such as data quality and the number of solvents on model performance. By leveraging atomic weight contributions, the model not only predicts these properties but also provides insights to guide structural modifications. In addition, we compiled and built a comprehensive database containing 36,756 records of fluorescence properties. To address the limitations of existing models in accurate prediction of Xanthene and Cyanine dyes, we then compiled 1148 Xanthene dye records and 1496 Cyanine dye records from the literature, comparing direct training with transfer learning approaches. The model achieved mean absolute errors (MAE) of 11.70 nm, 15.37 nm, 0.096, and 0.091 for predicting absorption wavelength (λ_abs), emission wavelength (λ_em), quantum yield (Φ) and molar extinction coefficient (Log(ε)), respectively. We integrated this work into a tool, Fluor-predictor, which supports comprehensive retrieval methods and multiproperty prediction. Fluor-predictor will facilitate data retrieval, prescreening, and structural modification of dyes.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"2854–2867 2854–2867"},"PeriodicalIF":5.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675839","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":"UMPPI: Unveiling Multilevel Protein-Peptide Interaction Prediction via Language Models.","authors":"Shuwen Xiong, Jiajie Cai, Hua Shi, Feifei Cui, Zilong Zhang, Leyi Wei","doi":"10.1021/acs.jcim.4c02365","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02365","url":null,"abstract":"<p><p>Protein-peptide interactions are essential to cellular processes and disease mechanisms. Identifying protein-peptide binding residues is critical for understanding peptide function and advancing drug discovery. However, experimental methods are costly and time-intensive, while existing computational approaches often predict interactions or binding residues separately, lack effective feature integration, or rely heavily on limited high-quality structural data. To address these challenges, we propose UMPPI (Unveiling Multilevel Protein-Peptide Interaction), a multiobjective framework based on the pretrained protein language model ESM2. UMPPI simultaneously predicts binary protein-peptide interactions and binding residues on both peptides and proteins through a multiobjective optimization strategy. By integrating ESM2 to encode sequences and extract latent structural information, UMPPI bridges the gap between sequence-based and structure-based methods. Extensive experiments demonstrated that UMPPI successfully captured binary interactions between peptides and proteins and identified the binding residues on peptides and proteins. UMPPI can serve as a useful tool for protein-peptide interaction prediction and identification of critical binding residues, thereby facilitating the peptide drug discovery process.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143612820","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":"scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering","authors":"Wenhao Wu,&nbsp;Shudong Wang*,&nbsp;Kuijie Zhang,&nbsp;Hengxiao Li,&nbsp;Sibo Qiao,&nbsp;Yuanyuan Zhang and Shanchen Pang,&nbsp;","doi":"10.1021/acs.jcim.4c0211410.1021/acs.jcim.4c02114","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c02114https://doi.org/10.1021/acs.jcim.4c02114","url":null,"abstract":"<p >Single-cell multiomics clustering integrates multiple omics data to analyze cellular heterogeneity and is crucial for uncovering complex biological processes and disease mechanisms. However, existing matched single-cell multiomics clustering methods often neglect the full utilization of intercellular relationships and the interactions and synergy between features from different omics, leading to suboptimal clustering performance. In this paper, we propose a deep collaborative contrastive learning framework for matched single-cell multiomics data clustering, named scMDCL. This framework fully leverages intercell relationships while enhancing feature interactions among identical cells across different omics data, thereby facilitating efficient clustering of multiomics data. Specifically, to fully utilize the topological information between cells, a graph autoencoder and a feature information enhancement module are designed for different omics, enabling the extraction and augmentation of cell features. Additionally, contrastive learning techniques are employed to strengthen the interactions among the different omics features of the same cell. Ultimately, multiomics deep collaborative clustering modules are utilized to achieve single-cell multiomics clustering. Extensive experiments conducted on nine publicly available single-cell multiomics datasets demonstrate the superior performance of the proposed framework in integrating multiomics data for clustering tasks.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"3048–3063 3048–3063"},"PeriodicalIF":5.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675749","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":"MOLGAECL: Molecular Graph Contrastive Learning via Graph Auto-Encoder Pretraining and Fine-Tuning Based on Drug–Drug Interaction Prediction","authors":"Yu Li*,&nbsp;Lin-Xuan Hou,&nbsp;Hai-Cheng Yi,&nbsp;Zhu-Hong You*,&nbsp;Shi-Hong Chen,&nbsp;Jia Zheng,&nbsp;Yang Yuan and Cheng-Gang Mi,&nbsp;","doi":"10.1021/acs.jcim.5c0004310.1021/acs.jcim.5c00043","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00043https://doi.org/10.1021/acs.jcim.5c00043","url":null,"abstract":"<p >Drug-drug interactions influence drug efficacy and patient prognosis, providing substantial research value. Some existing methods struggle with the challenges posed by sparse networks or lack the capability to integrate data from multiple sources. In this study, we propose MOLGAECL, a novel approach based on graph autoencoder pretraining and molecular graph contrastive learning. Initially, a large number of unlabeled molecular graphs are pretrained using a graph autoencoder, where graph contrastive learning is applied for more accurate representation of the drugs. Subsequently, a full-parameter fine-tuning is performed on different data sets to adapt the model for drug interaction-related prediction tasks. To assess the effectiveness of MOLGAECL, comparison experiments with state-of-the-art methods, fine-tuning comparison experiments, and parameter sensitivity analysis are conducted. Extensive experimental results demonstrate the superior performance of MOLGAECL. Specifically, MOLGAECL achieves an average increase of 6.13% in accuracy, 6.14% in AUROC, and 8.16% in AUPRC across all data sets.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"3104–3116 3104–3116"},"PeriodicalIF":5.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675737","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":"CMDmpnn: Combining Comparative Molecular Dynamics and ProteinMPNN to Rapidly Expand Enzyme Substrate Spectrum","authors":"Chuan-qi Sun,&nbsp;Zhi-min Li*,&nbsp;Yu Ji*,&nbsp;Ulrich Schwaneberg and Zong-lin Li*,&nbsp;","doi":"10.1021/acs.jcim.5c0011710.1021/acs.jcim.5c00117","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00117https://doi.org/10.1021/acs.jcim.5c00117","url":null,"abstract":"<p >Expanding enzyme substrate spectra enhances industrial applications and drives sustainable biocatalysis. Despite advances, challenges in modification efficiency and high-throughput screening persist. Here, we developed a virtual screening method called CMDmpnn that combines comparative molecular dynamics (MD) simulations and ProteinMPNN to broaden enzyme substrate spectra without compromising other industrially important properties of enzymes, such as thermostability. Using glycosyltransferase as a model, we first established a dynamic model library of the wild-type enzyme through MD simulations and performed clustering. Subsequently, we utilized ProteinMPNN to generate a comprehensive set of new sequences for the entire library, enabling rapid identification of all possible enzyme variants. Short MD simulations were then conducted on variant–substrate complex models, with results compared to those of the wild-type enzyme. By analyzing catalytically relevant information such as substrate binding modes and key atomic distances, we identified multiple variants capable of catalyzing a broad spectrum of phenolic compounds, all within a timeframe of less than 2 weeks. The CMDmpnn method offers a powerful and efficient tool for rapidly expanding enzyme substrate spectra.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"65 6","pages":"2741–2747 2741–2747"},"PeriodicalIF":5.6,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675819","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}