Journal of Chemical Information and Modeling 最新文献

Multirelational Hypergraph Representation Learning for Predicting circRNA-miRNA Associations 预测 circRNA-miRNA 关联的多关系超图表示学习

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-21 DOI: 10.1021/acs.jcim.4c01436

Wenjing Yin, Shudong Wang, Yuanyuan Zhang, Sibo Qiao, Wenhao Wu, Hengxiao Li

{"title":"Multirelational Hypergraph Representation Learning for Predicting circRNA-miRNA Associations","authors":"Wenjing Yin, Shudong Wang, Yuanyuan Zhang, Sibo Qiao, Wenhao Wu, Hengxiao Li","doi":"10.1021/acs.jcim.4c01436","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01436","url":null,"abstract":"One of the principal functions of circular RNA (circRNA) is to participate in gene regulation by sponging microRNAs (miRNAs). Using accumulated circRNA-miRNA associations (CMAs) to construct computational models for predicting potential associations provides a crucial tool for accelerating the validation of reliable associations through traditional experiments. Nevertheless, the current prediction models are constrained in their capacity to represent the higher-order relationships of CMAs and thus require further enhancement in terms of their predictive efficacy. In order to address this issue, we propose a new model based on multirelational hypergraph representation learning (MRHRL). This model employs hypergraphs to capture various higher-order relationships among RNAs and aggregates complementary information through a view attention mechanism. Furthermore, MRHRL introduces a hyperedge-level reconstruction task, jointly optimizing the prediction and reconstruction tasks within a unified framework to uncover potential information, thereby enhancing the model’s predictive and generalization capabilities. Experiments conducted on three real-world data sets demonstrate that MRHRL achieves satisfactory results in CMAs prediction, significantly outperforming existing prediction models.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452083","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}

引用次数: 0

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning 通过图同构网络进行多模态表征学习，实现毒性多任务学习

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-21 DOI: 10.1021/acs.jcim.4c01061

Guishen Wang, Hui Feng, Mengyan Du, Yuncong Feng, Chen Cao

{"title":"Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning","authors":"Guishen Wang, Hui Feng, Mengyan Du, Yuncong Feng, Chen Cao","doi":"10.1021/acs.jcim.4c01061","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01061","url":null,"abstract":"Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model’s superior predictive capability and robustness.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452082","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}

引用次数: 0

Ramachandran-like Conformational Space for DNA DNA 的拉马钱德兰式构象空间

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-18 DOI: 10.1021/acs.jcim.4c01294

Gabriela da Rosa, Leandro Grille, Pablo D. Dans

{"title":"Ramachandran-like Conformational Space for DNA","authors":"Gabriela da Rosa, Leandro Grille, Pablo D. Dans","doi":"10.1021/acs.jcim.4c01294","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01294","url":null,"abstract":"DNA’s ability to exist in a wide variety of structural forms, subforms, and secondary motifs is fundamental to numerous biological processes and has driven the development of biotechnological applications. Major determinants of DNA flexibility are the multiple torsional degrees of freedom around the phosphodiester backbone. This high complexity can be rationalized by using two pseudotorsional angles linking atoms P and C4′, from which Ramachandran-like plots can be built. In this contribution, we explore the distribution of η (eta: C4′i–1-Pi-C4′i-Pi+1) and θ (theta: Pi-C4′i-Pi+1-C4′i+1) angles in known experimental structures retrieved from the Protein Data Bank (PDB), subdividing the conformational space into different datasets. After the removal of the canonical/helical conformations typical of the B-form, we find the existence of a conformational map with clearly permitted and forbidden regions. Some of these regions are populated with specific DNA forms, like Z- or A-DNA, or by specific secondary motifs, like G-quadruplexes and junctions. We evaluated the sequence dependency and energy relationship among the high-density regions identified in the η–θ space. Furthermore, we analyzed the effect produced by proteins and cations when bound to DNA, finding that specific proteins produce some nonhelical conformations, while other regions appear to be stabilized by divalent cations.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448502","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}

引用次数: 0

Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D 利用沿正常模式的增强采样探索隐匿口袋：KRAS G12D 案例研究

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-17 DOI: 10.1021/acs.jcim.4c01435

Neha Vithani, She Zhang, Jeffrey P. Thompson, Lara A. Patel, Alex Demidov, Junchao Xia, Alexander Balaeff, Ahmet Mentes, Yelena A. Arnautova, Anna Kohlmann, J. David Lawson, Anthony Nicholls, A. Geoffrey Skillman, David N. LeBard

{"title":"Exploration of Cryptic Pockets Using Enhanced Sampling Along Normal Modes: A Case Study of KRAS G12D","authors":"Neha Vithani, She Zhang, Jeffrey P. Thompson, Lara A. Patel, Alex Demidov, Junchao Xia, Alexander Balaeff, Ahmet Mentes, Yelena A. Arnautova, Anna Kohlmann, J. David Lawson, Anthony Nicholls, A. Geoffrey Skillman, David N. LeBard","doi":"10.1021/acs.jcim.4c01435","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01435","url":null,"abstract":"Identification of cryptic pockets has the potential to open new therapeutic opportunities by discovering ligand binding sites that remain hidden in static apo structures of a target protein. Moreover, allosteric cryptic pockets can become valuable for designing target-selective ligands when the natural ligand binding sites are conserved in variants of a protein. For example, before an allosteric cryptic pocket was discovered, KRAS was considered undruggable due to its smooth surface and conservation of the GDP/GTP binding pocket across the wild type and oncogenic isoforms. Recent identification of the Switch-II cryptic pocket in the KRASG12C mutant and FDA approval of anticancer drugs targeting this site underscores the importance of cryptic pockets in solving pharmaceutical challenges. Here, we present a newly developed approach for the exploration of cryptic pockets using weighted ensemble molecular dynamics simulations with inherent normal modes as progress coordinates applied to the wild type KRAS and the G12D mutant. We performed extensive all-atomic simulations (>400 μs) with and without several cosolvents (xenon, ethanol, benzene), and analyzed trajectories using three distinct methods to search for potential binding pockets. These methods have been applied as a proof-of-concept to KRAS and have shown they can predict known cryptic binding sites. Furthermore, we performed ligand-binding simulations of a known inhibitor (MRTX1133) to shed light on the nature of cryptic pockets in KRASG12D and the role of conformational selection vs induced-fit mechanism in the formation of these cryptic pockets.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142448503","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}

引用次数: 0

Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models 利用专家产品化学语言模型导航超大型虚拟化学空间

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-16 DOI: 10.1021/acs.jcim.4c01214

Shuya Nakata, Yoshiharu Mori, Shigenori Tanaka

{"title":"Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models","authors":"Shuya Nakata, Yoshiharu Mori, Shigenori Tanaka","doi":"10.1021/acs.jcim.4c01214","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01214","url":null,"abstract":"Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood–brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440466","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}

引用次数: 0

Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations 利用绝对结合自由能计算分析糖蛋白 A 的识别能力

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-16 DOI: 10.1021/acs.jcim.4c01088

Sondos Musleh, Irfan Alibay, Philip C. Biggin, Richard A. Bryce

{"title":"Analysis of Glycan Recognition by Concanavalin A Using Absolute Binding Free Energy Calculations","authors":"Sondos Musleh, Irfan Alibay, Philip C. Biggin, Richard A. Bryce","doi":"10.1021/acs.jcim.4c01088","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01088","url":null,"abstract":"Carbohydrates are key biological mediators of molecular recognition and signaling processes. In this case study, we explore the ability of absolute binding free energy (ABFE) calculations to predict the affinities of a set of five related carbohydrate ligands for the lectin protein, concanavalin A, ranging from 27-atom monosaccharides to a 120-atom complex-type N-linked glycan core pentasaccharide. ABFE calculations quantitatively rank and estimate the affinity of the ligands in relation to microcalorimetry, with a mean signed error in the binding free energy of −0.63 ± 0.04 kcal/mol. Consequently, the diminished binding efficiencies of the larger carbohydrate ligands are closely reproduced: the ligand efficiency values from isothermal titration calorimetry for the glycan core pentasaccharide and its constituent trisaccharide and monosaccharide compounds are respectively −0.14, −0.22, and −0.41 kcal/mol per heavy atom. ABFE calculations predict these ligand efficiencies to be −0.14 ± 0.02, −0.24 ± 0.03, and −0.46 ± 0.06 kcal/mol per heavy atom, respectively. Consequently, the ABFE method correctly identifies the high affinity of the key anchoring mannose residue and the negligible contribution to binding of both β-GlcNAc arms of the pentasaccharide. While challenges remain in sampling the conformation and interactions of these polar, flexible, and weakly bound ligands, we nevertheless find that the ABFE method performs well for this lectin system. The approach shows promise as a quantitative tool for predicting and deconvoluting carbohydrate–protein interactions, with potential application to design of therapeutics, vaccines, and diagnostics.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440465","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}

引用次数: 0

Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents 利用不同溶剂中的核磁共振数据验证大环化合物的小分子力场

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-15 DOI: 10.1021/acs.jcim.4c01120

Franz Waibl, Fabio Casagrande, Fabian Dey, Sereina Riniker

{"title":"Validating Small-Molecule Force Fields for Macrocyclic Compounds Using NMR Data in Different Solvents","authors":"Franz Waibl, Fabio Casagrande, Fabian Dey, Sereina Riniker","doi":"10.1021/acs.jcim.4c01120","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01120","url":null,"abstract":"Macrocycles are a promising class of compounds as therapeutics for difficult drug targets due to a favorable combination of properties: They often exhibit improved binding affinity compared to their linear counterparts due to their reduced conformational flexibility, while still being able to adapt to environments of different polarity. To assist in the rational design of macrocyclic drugs, there is need for computational methods that can accurately predict conformational ensembles of macrocycles in different environments. Molecular dynamics (MD) simulations remain one of the most accurate methods to predict ensembles quantitatively, although the accuracy is governed by the underlying force field. In this work, we benchmark four different force fields for their application to macrocycles by performing replica exchange with solute tempering (REST2) simulations of 11 macrocyclic compounds and comparing the obtained conformational ensembles to nuclear Overhauser effect (NOE) upper distance bounds from NMR experiments. Especially, the modern force fields OpenFF 2.0 and XFF yield good results, outperforming force fields like GAFF2 and OPLS/AA. We conclude that REST2 in combination with modern force fields can often produce accurate ensembles of macrocyclic compounds. However, we also highlight examples for which all examined force fields fail to produce ensembles that fulfill the experimental constraints.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436427","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}

引用次数: 0

HexagonRingCalculator: A Handy Code for Hexagonal Ring Characterization in Atomistic Simulations HexagonRingCalculator：原子模拟中六角环特征描述的便捷代码

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-15 DOI: 10.1021/acs.jcim.4c01432

Yulei Wang, Kaiqiang He, Dehua Dong, Jinxing Gu, Jefferson Zhe Liu, Yuxiang Wang

引用次数: 0

An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications 脚手架识别和命名系统 (SCINS) 的开源实现和应用实例

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-15 DOI: 10.1021/acs.jcim.4c01314

Kamen P. Petrov, Andreas Bender

{"title":"An Open-Source Implementation of the Scaffold Identification and Naming System (SCINS) and Example Applications","authors":"Kamen P. Petrov, Andreas Bender","doi":"10.1021/acs.jcim.4c01314","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c01314","url":null,"abstract":"Organizing and partitioning sets of chemical structures is of considerable practical significance, e.g., in compound library analysis and the postprocessing of screening hit lists. Approaches such as unsupervised clustering are computationally demanding and dataset-dependent; on the other hand, rule-based methods, such as those based on Murcko scaffolds, have linear time complexity but are often too fine-grained, leading to a large number of singletons or sparsely populated classes. An alternative rule-based method that seeks to achieve an optimal balance when grouping compounds into sets is the ‘Scaffold Identification and Naming System’ (SCINS). To facilitate public use of this previously published method, here, we provide an open-source Python implementation of SCINS, dependent only on RDKit. We show that SCINS can be useful in identifying sparsely and densely populated regions in chemical space in large databases, here exemplified with Enamine REAL Diverse and ChEMBL. We find that Enamine REAL Diverse covers a much smaller SCINS space relative to ChEMBL, whereas the opposite is true when Murcko and generic Murcko scaffolds are considered. Additionally, we show that SCINS can result in chemically intuitive grouping of medium-sized sets of bioactive compounds, which can be useful in compound selection from virtual screening campaigns as well as postprocessing of experimental hit lists. Hence, in this work, we provide both an open-source implementation of SCINS and its characterization with relevant use cases.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436440","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}

引用次数: 0

In Silico Design of Novel RGS2–Galpha-q Interaction Inhibitors with Anticancer Activity 具有抗癌活性的新型 RGS2-Galpha-q 相互作用抑制剂的硅学设计

IF 5.6 2区化学

Journal of Chemical Information and Modeling Pub Date : 2024-10-14 DOI: 10.1021/acs.jcim.4c00932

Adam Bair, Natalie Printy, So Hee Choi, Joshua Wilkinson, Joseph O’Brien, Brian Myers, David Roman, Tarek M. Mahfouz

{"title":"In Silico Design of Novel RGS2–Galpha-q Interaction Inhibitors with Anticancer Activity","authors":"Adam Bair, Natalie Printy, So Hee Choi, Joshua Wilkinson, Joseph O’Brien, Brian Myers, David Roman, Tarek M. Mahfouz","doi":"10.1021/acs.jcim.4c00932","DOIUrl":"https://doi.org/10.1021/acs.jcim.4c00932","url":null,"abstract":"Regulators of G-protein signaling (RGS) are a family of approximately 30 proteins that bind to and deactivate the alpha subunits of G-proteins (Gα) by accelerating their GTP hydrolysis rates, which terminates G-protein coupled receptor (GPCR) signaling. Thus, RGS proteins are essential in regulating GPCR signaling, and most members are implicated as critical nodes in human diseases such as hypertension, depression, and others. Regulator of G-protein signaling 2 (RGS2), a member of the R4 family of RGS proteins, is overexpressed in many solid breast cancers, and its levels in prostate cancer significantly correlate with the metastatic stage and poor prognosis. We sought to develop RGS2 inhibitors as potential chemotherapeutic agents utilizing structure-based drug design approaches. Available structures of the RGS2-Gα complex were used to extract a pharmacophore model for searching chemical databases. Docking of identified hits to RGS2 as well as other RGS structures was used to screen the hits for potent and selective RGS2 inhibitors. Whole cell assays showed the top 10 ranking compounds, AJ-1–AJ-10, to inhibit RGS2–Gαq interactions. Differential scanning fluorimetry showed AJ-3 to bind RGS2 but not Gαq. All 10 compounds inhibited the growth of several RGS2 expressing cancers in cell culture assays. In addition, AJ-3 inhibited the migration of LNCaP prostate cancer cells in wound healing assays. This is the first group of RGS2 inhibitors identified by structure-based approaches and that show anticancer activity. These results highlight the potential RGS2 inhibitors have to be a new class of chemotherapeutic agents.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431792","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}

引用次数: 0