Journal of Chemical Information and Modeling 最新文献

{"title":"Minimum Energy Pathway for Lesion Recognition and DNA Binding by RAD4/XPC.","authors":"Aadarsh Raghunathan,Marimuthu Krishnan","doi":"10.1021/acs.jcim.5c00738","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00738","url":null,"abstract":"The pyrimidine-pyrimidone (6-4) photoproduct (6-4PP) is a UV-induced DNA lesion implicated in skin disorders and cancers. The repair protein XPC/RAD4 detects this lesion and initiates nucleotide excision repair to safeguard genomic integrity. While the X-ray crystallographic structure of the 6-4PP containing DNA-RAD4/XPC complex reveals DNA distortion and extrusion of the lesion and partner bases, the precise mechanism by which RAD4/XPC initiates lesion repair remains unclear. To investigate this, we employed molecular dynamics simulations, umbrella sampling, and the nudged elastic band method to map the minimum energy path (MEP) from RAD4's initial encounter with damaged DNA to its bound state. Our results reveal that the initial interrogation phase involves partial opening of the DNA, marked by the partial extrusion of the lesion while its partner bases largely remain intrahelical, accompanied by significant DNA unwinding near the damage site. This partially opened state represents the rate-limiting distortion, with 5' base flipping as the bottleneck for downstream events leading to the bound state. Upon overcoming the bottleneck, the DNA adopts a final untwisted conformation, with the lesion flipping out first, followed by the complete sequential flipping of the 5' base and then the 3' partner bases, while full β-hairpin insertion ultimately stabilizes the final bound DNA complex. The energetics and structural intermediates along the MEP provide key insights into conformational changes that drive lesion recognition, extrusion, and stable binding, advancing our understanding of nucleotide excision repair.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"14 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140074","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":"The \"Carpet-like\" Mechanism of NaD1 on Candida albicans Cell Membranes at Low NaD1/Lipid Ratios Revealed by Molecular Dynamics Simulations.","authors":"Yueru Zhao,Ximeng Sun,Mengmeng Yuan,Zhixuan Fang,Hua Yu","doi":"10.1021/acs.jcim.5c01008","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01008","url":null,"abstract":"NaD1, a plant defensin isolated from the flowers of Nicotiana alata, is a cationic antimicrobial peptide (CAP) that displays potent antifungal activity against a variety of pathogenic fungi and tumor cells by attacking cell membranes. Specific interactions between NaD1 and phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2), as well as NaD1 and phosphatidic acid (PA), have been revealed to trigger the assembly of NaD1-lipid oligomers that damage cell membranes. In this study, to comprehensively understand NaD1's interactions with diverse membrane phospholipids and their collective impact on membrane architecture, coarse-grained (CG) self-assembly molecular dynamics (MD) simulations and all-atom MD simulations were conducted to investigate interactions of NaD1 oligomeric states (monomer, dimer, and tetramer) with a Candida albicans membrane model containing physiological phospholipid compositions (POPC/POPE/POPI/POPS/POPG/POPA/DPP2 = 40:27:18:7:1:6:1 molar ratio) using GROMACS with Martini and charmm36m force fields. The results showed that phospholipids spontaneously formed bilayer structures with the NaD1 oligomer bound to their surfaces. The NaD1 monomer adopted two distinct binding orientations on the membrane, whereas both the dimer and tetramer adopted a single identical orientation. NaD1 exhibited strongest interfacial binding to DPP2 (model PIP2) and POPI, moderate affinity for POPE/POPC/POPS/POPA, and negligible interaction with POPG. Further calculation showed interface phospholipid redistribution following NaD1 binding, which featured increased DPP2/POPI/POPA and decreased POPC/POPE/POPS/total phospholipids versus NaD1-free membranes, quantitatively showing the perturbation of NaD1 on the membrane structure, together with the membrane thickness reduction and area per lipid (APL) increase (from monomer system, dimer system, to tetramer system) revealed by fundamental bilayer property analysis. These results revealed the \"carpet-like\" mechanism of NaD1 on C. albicans cell membranes at low NaD1/lipid ratios. In addition, this study also revealed the key binding sites of DPP2 (Lys4, His33, Ser35-Leu38, and Arg40, reproducing the hydrogen-bond network in the crystal structure), the contribution of POPI (besides DPP2) to NaD1 dimerization, and the role of POPE and POPS (besides POPA) in promoting \"side-by-side\" NaD1 tetramer formation. The spontaneous formation of NaD1 \"head-to-head\" tetramers was observed for the first time, providing new insights into the process of NaD1 \"carpet-like\" structure formation. Overall, the molecular details in this study provide us a better understanding of the \"carpet-like\" model of the NaD1 antimicrobial mechanism.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134563","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":"Modular and Interoperable Workflows for Benchmarking Alchemical Binding Free Energy Calculation Methodologies.","authors":"Anna M Herz, Maicol Bissaro, Carmen Esposito, Julien Michel","doi":"10.1021/acs.jcim.5c01493","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01493","url":null,"abstract":"<p><p>Alchemical free energy methods are gaining traction in computer-aided drug discovery. An expanding array of methodologies is available for the setup, execution, and analysis of relative binding free energy (RBFE) calculations. However, the sharing of algorithms and protocols developed by different organizations is often impeded by incompatible software and outdated file formats. In this work, we leveraged the BioSimSpace framework to build modular and interoperable RBFE workflows. We assessed the performance of various setup, simulation, and analysis tools developed by the community on a benchmark set of six protein-ligand congeneric series, providing recommendations on best practices for the reliable application of RBFE methods in drug discovery.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145147166","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":"Integrated Machine Learning and Structure-Based Virtual Screening Identify Osimertinib as a TNIK Inhibitor for Idiopathic Pulmonary Fibrosis.","authors":"Likun Zhao,Huanxiang Liu,Xiaojun Yao,Xiuling Ma,Bo Liu,Bin Li,Henry H Y Tong,Qianqian Zhang","doi":"10.1021/acs.jcim.5c01521","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01521","url":null,"abstract":"Traf2-and Nck-interacting kinase (TNIK) has been implicated in fibrosis-associated signaling pathways and has recently emerged as a promising therapeutic target for idiopathic pulmonary fibrosis (IPF). In this study, we employed an integrated strategy combining machine learning-based prediction and structure-based virtual screening to repurpose drugs from the DrugBank database as potential TNIK inhibitors for IPF treatment. Using this approach, we identified 19 candidate compounds, among which 14 demonstrated TNIK enzymatic inhibition rates exceeding 70% at a concentration of 10 μM, as determined by the ADP-Glo assay. Notably, among these candidates, the approved drug osimertinib showed potent TNIK inhibitory activity with an IC50 of 151.90 nM and demonstrated an acceptable cytotoxicity profile in human lung fibroblast MRC-5 cells (CC50 = 4366.01 nM). Furthermore, osimertinib significantly suppressed TGF-β1-induced fibrogenesis in human lung fibroblast-derived MRC-5 cells at 3 μM, as confirmed by qPCR and Western blot analyses. Molecular dynamics simulations and structural analyses revealed that osimertinib engages the ATP-binding pocket of TNIK via hinge hydrogen bonding with Cys108, while unoccupied subpockets near Met105 and the involvement of Gln157 provide opportunities for rational modifications to improve affinity and selectivity. These findings demonstrate the robustness of our integrated machine learning and structure-based virtual screening pipeline and suggest that osimertinib warrants further evaluation as a TNIK-targeted agent for IPF, with future studies needed to optimize its potency and selectivity.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"42 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140477","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":"Robust Prediction of Protein-Ligand Binding Potency with Multi-modal Customized Gate Control.","authors":"Bofei Xu, Wenting Tang, Danial Muhammad, Yuqi Yin, Zhirong Liu, Zhaoxi Sun","doi":"10.1021/acs.jcim.5c01668","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01668","url":null,"abstract":"<p><p>The main protease (Mpro) is a critical target in the design of antiviral drugs against coronaviruses, while accurately predicting the binding affinity between small molecules and this target remains a key challenge. In the recent Polaris challenge of blind drug-potency prediction targeting SARS-CoV-2 and MERS-CoV Mpro, we developed a multimodal multitask graph attention network based on the customized gate control framework (abbreviated as MultiMolCGC). Our team achieved top performance among all participating teams in the blind prediction challenge. In this paper, we detail the model development and further explorations in terms of pretraining, adjusting the model architecture, and many others. Our model consistently outperforms traditional machine learning baselines, demonstrating the effectiveness of end-to-end deep learning in capturing complex molecular interactions. Integrating multimodal representations proved essential, and the multitask specialized gating architecture outperformed both single-task and nonspecialized multitask variants, highlighting the value of tailored knowledge sharing. While auxiliary loss weighting and hyperparameter tuning offered modest improvements, incorporating predicted structural data unexpectedly reduced performance, likely due to structural uncertainty. Notably, pretraining on large-scale synthetic docking data sets significantly enhanced performance in low-data scenarios, reducing dependence on experimental pIC₅₀ data. The numerical results highlight the potential of MultiMolCGC as a robust and accurate deep-learning framework for protein-ligand binding in future studies.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145135923","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":"Benchmarking Machine Learning Models for HIV-1 Protease Inhibitor Resistance Prediction: Impact of Data Set Construction and Feature Representation.","authors":"Rocío Lucía Beatriz Riveros Maidana,Lucas de Almeida Machado,Ana Carolina Ramos Guimarães","doi":"10.1021/acs.jcim.5c01544","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01544","url":null,"abstract":"The rapid emergence of drug resistance in viral infections represents a significant global health challenge, threatening the efficacy of treatments for multiple diseases. Machine learning models have emerged as valuable tools for predicting antiviral drug resistance from genomic data, with HIV-1 protease serving as a well-characterized model system due to its extensive experimental data and clinical relevance. Here, we systematically evaluate multiple previously published HIV-1 protease inhibitor (PI) resistance prediction models across three distinct data sets with different preprocessing and ambiguous sequencing processing strategies and propose a new approach for preprocessing. We tested Steiner's data set (n = 1540) with first-amino-acid selection at ambiguous positions, Shen's expanded data set (n = 500,390) with all possible combinations at ambiguous positions, and our In-house data set (n = 869) with strict exclusion of ambiguous sequences. We compare neural networks architectures (Multilayer Perceptron, Bidirectional Recurrent Neural Network, and Convolutional Neural Network), traditional machine learning models (Random Forest and K-Nearest Neighbor), and logistic regression using either zScales physicochemical descriptors or Rosetta energy terms. Sequence expansion preprocessing can artificially increase performance metrics (mean AUC: 0.986-0.999) by creating substantial redundancy (99.6% of expanded data set consists of duplicated sequences from 2096 unique originals), while our clustering-based validation approach provides a more stringent assessment of model generalizability. Remarkably, our physicochemically informed logistic regression models achieved performance comparable to complex neural networks on challenging test sets (zScales LR: AUC = 0.973; Rosetta LR: AUC = 0.944), while offering superior interpretability. Furthermore, the zScales LR model offered significantly greater computational efficiency (0.007 s/prediction) compared to that of Rosetta LR (776.117 s/prediction). Mutual information analysis revealed distinct complementary resistance mechanisms: The zScales descriptors identified discrete resistance hotspots at positions 10, 46, 54, 71, and 90, while the Rosetta energy terms revealed interconnected energetic networks across structurally adjacent residues, particularly in functionally critical flap regions (positions 46-54). This study demonstrates how data set construction choices directly impact apparent model performance while establishing that well-chosen physicochemical feature representations can match or exceed complex neural networks for HIV-1 PI resistance modeling, offering both accuracy and mechanistic interpretability critical for clinical implementation and drug development.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"18 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140075","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":"Molecular Property Prediction Based on Improved Graph Transformer Network and Multitask Joint Learning Strategy.","authors":"Xin Zhao,Shuyi Zhang,Tao Zhang,Haotong Li,Yahui Cao","doi":"10.1021/acs.jcim.5c01339","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01339","url":null,"abstract":"Molecular property prediction is of great significance in drug design and materials science. However, due to the complexity and diversity of molecular structures, existing methods often struggle to simultaneously capture both the local chemical environments and the global structural characteristics of molecules, and they lack generalization ability when dealing with multiple data sets. To address these challenges, this paper proposes a molecular property prediction approach based on an improved Graph Transformer network combined with a multitask joint learning strategy. Specifically, we enhance the attention mechanism by integrating atomic relative position encoding and bond information encoding, thereby explicitly incorporating spatial structure and chemical bond features into the model. Meanwhile, we construct a hierarchical feature extraction architecture by alternately stacking local message-passing layers and global attention layers, and we adopt a mixture-of-experts mechanism to achieve collaborative representation of both local molecular features and global structure. In addition, we design a multitask joint learning strategy that leverages alternating training on multiple tasks and dynamic weighting adjustments to significantly improve the model's generalization performance across diverse data sources. Experimental results show that our method achieves higher prediction accuracy on multiple classification and regression data sets, with an average improvement of 6.4% and 16.7% over baseline methods. Compared with single-data set training, our multitask joint learning strategy further boosts the prediction accuracy by an average of 2.8% and 6.2%. These findings indicate that the proposed approach is highly effective in predicting a wide range of molecular properties.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"43 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127388","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":"ROSHAMBO2: Accelerating Molecular Alignment for Large Chemical Libraries with GPU Optimization and Algorithmic Advances.","authors":"Rasha Atwi,Stephen Farr,Ye Wang,Adam Antoszewski,Simone Sciabola","doi":"10.1021/acs.jcim.5c01322","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01322","url":null,"abstract":"Molecular alignment and 3D similarity are crucial tasks in computational drug discovery, enabling applications such as virtual screening and pharmacophore modeling. ROSHAMBO, an open-source package for optimizing molecular alignment using Gaussian volume overlaps, demonstrated near-state-of-the-art performance and accuracy across multiple target classes. However, its computational efficiency has been a limiting factor in the virtual screening of ultralarge chemical libraries. To address this limitation, we introduce ROSHMABO2, an optimized version that achieves a greater than 200-fold improvement in performance over the original ROSHAMBO implementation through algorithmic innovations, GPU acceleration, and optimized memory handling. This performance establishes ROSHMABO2 as an ideal tool for high-throughput applications, such as virtual screening and chemical library design, enabling efficient exploration of large chemical spaces. In addition to its computational enhancements, the new version retains its modularity, accessibility, and compatibility with diverse workflows. These improvements position ROSHAMBO2 as a transformative tool for modern cheminformatics, addressing the growing demands for scalable molecular modeling. ROSHAMBO2 is accessible at https://github.com/molecularinformatics/roshambo2 and is available for use under the MIT license.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"58 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127386","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":"DeepExpDR: Drug Response Prediction through Molecular Topological Grouping and Substructure-Aware Expert.","authors":"Yuanpeng Zhang,Zhijian Huang,Yurong Qian,Peng Xie,Ziyu Fan,Min Wu,Lei Deng","doi":"10.1021/acs.jcim.5c01476","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01476","url":null,"abstract":"Cancer remains a major threat to human health. Tumor heterogeneity often leads to differences in tumor growth rate, invasion capacity, drug sensitivity, and prognosis, which complicates treatment strategies. Currently, drug responses are often verified through time-consuming and costly biological experiments, hindering the development of anticancer drug and precision medicine. With advancements in deep learning, various models for drug response prediction have been proposed. However, few of them take into account the impact of molecular topological properties on drug feature extraction and drug response prediction. In this study, we present DeepExpDR, a deep expert framework designed for drug response prediction. We first pretrain a self-supervised clustering model to group drugs based on their molecular scaffold similarities and then assign each drug group to a specialized substructure-aware expert. Each expert incorporates a substructure sensing network, which predicts drug response information from substructure sequences, cancer cell transcriptional gene expression values, and drug response correlation matrices. Finally, the predicted responses from experts are weighted summed to generate the final IC50 value. Experimental results demonstrate that DeepExpDR achieves state-of-the-art performance in both warm and cold settings, across regression and classification tasks. Our case study further verifies the effectiveness of DeepExpDR for detecting unknown cancer drug responses. Data and codes are available on https://github.com/ZYPssss/DeepExpDR.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"9 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117115","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":"A Scalable and Generalizable Method to Minimize Solvent Interference in Identification of Chemical Reaction Networks from Spectroscopic Data.","authors":"Kuldeep Singh,Karthik Srinivasan,Ziting Sun,Jing Liu,Vinay Prasad","doi":"10.1021/acs.jcim.5c01553","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c01553","url":null,"abstract":"Challenges such as varying levels of solvent interference that obscure spectral bands restrict the applicability and direct adoption of spectroscopic techniques for the analysis and characterization of complex reacting systems. In this work, we develop a generic and scalable method to minimize solvent interference on the spectroscopic signatures of reacting mixtures under varying process conditions without prior information about the constituents. The method frames solvent effect minimization as a tensorial factorization problem to segregate the solute and solvent contributions (i.e., latent factors) across each data dimension. We employ two distinct methodologies, named the direct and orthogonal approaches, to distinguish between the solute and the solvent latent factors. Comparative analyses on four case studies with spectroscopic process data show the efficiency of the proposed methods in minimizing and extracting useful information from obscured bands. The extracted solvent-free latent factors can be reconstructed to provide solvent-free spectroscopic data or directly applied to tasks such as mixture characterization, impurity detection, predictive modeling, and data mining. In this work, we apply them to generate plausible reaction networks for various chemical systems. The proposed approaches generalize to any solvent and adapt to the large process data sets typically found in chemical process industries.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"39 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103507","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}