Journal of Chemical Information and Modeling 最新文献

{"title":"Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.","authors":"Yaxing Wang, Xiang Liu, Yipeng Zhang, Xiangjun Wang, Kelin Xia","doi":"10.1021/acs.jcim.4c02309","DOIUrl":"10.1021/acs.jcim.4c02309","url":null,"abstract":"<p><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":" ","pages":"2785-2793"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622863","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":"Leveraging Transfer Learning for Predicting Protein-Small-Molecule Interaction Predictions.","authors":"Jian Wang, Nikolay V Dokholyan","doi":"10.1021/acs.jcim.4c02256","DOIUrl":"10.1021/acs.jcim.4c02256","url":null,"abstract":"<p><p>A complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼10⁴ proteins in a typical human cell offer plausible 10⁸ interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete data sets (10³-10⁴ protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small data sets. Yuel 2 is pretrained on a large-scale data set to learn intricate structural features and then fine-tuned on specialized data sets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics, <i>K</i>_d, <i>K</i>_i, and IC₅₀, between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699095","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":"FEP-SPell-ABFE: An Open-Source Automated Alchemical Absolute Binding Free-Energy Calculation Workflow for Drug Discovery.","authors":"Pengfei Li, Tingting Pu, Ye Mei","doi":"10.1021/acs.jcim.4c01986","DOIUrl":"10.1021/acs.jcim.4c01986","url":null,"abstract":"<p><p>The binding affinity between a drug molecule and its target, measured by the absolute binding free energy (ABFE), is a crucial factor in the lead discovery phase of drug development. Recent research has highlighted the potential of in silico ABFE predictions to directly aid drug development by allowing for the ranking and prioritization of promising candidates. This work introduces an open-source Python workflow called FEP-SPell-ABFE, designed to automate ABFE calculations with minimal user involvement. The workflow requires only three key inputs: a receptor protein structure in PDB format, candidate ligands in SDF format, and a configuration file (config.yaml) that governs both the workflow and molecular dynamics simulation parameters. It produces a ranked list of ligands along with their binding free energies in the comma-separated values (CSV) format. The workflow leverages SLURM (Simple Linux Utility for Resource Management) for automating task execution and resource allocation across the modules. A usage example and several benchmark systems for validation are provided. The FEP-SPell-ABFE workflow, along with a practical example, is publicly accessible on GitHub at https://github.com/freeenergylab/FEP-SPell-ABFE, distributed under the MIT License.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2711-2721"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143539496","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":"Disentangling Folding from Energetic Traps in Simulations of Disordered Proteins.","authors":"Jeffrey M Lotthammer, Alex S Holehouse","doi":"10.1021/acs.jcim.4c02005","DOIUrl":"10.1021/acs.jcim.4c02005","url":null,"abstract":"<p><p>Protein conformational heterogeneity plays an essential role in a myriad of different biological processes. Extensive conformational heterogeneity is especially characteristic of intrinsically disordered proteins and protein regions (collectively IDRs), which lack a well-defined three-dimensional structure and instead rapidly exchange between a diverse ensemble of configurations. An emerging paradigm recognizes that the conformational biases encoded in IDR ensembles can play a central role in their biological function, necessitating understanding these sequence-ensemble relations. All-atom simulations have provided critical insight into our modern understanding of the solution behavior of IDRs. However, effectively exploring the accessible conformational space associated with large, heterogeneous ensembles is challenging. In particular, identifying poorly sampled or energetically trapped regions of disordered proteins in simulations often relies on qualitative assessment based on visual inspection of simulations and/or analysis data. These approaches, while convenient, run the risk of masking poorly sampled simulations. In this work, we present an algorithm for quantifying per-residue local conformational heterogeneity in protein simulations. Our work builds on prior work and compares the similarity between backbone dihedral angle distributions generated from molecular simulations in a limiting polymer model and across independent all-atom simulations. In this regime, the polymer model serves as a statistical reference model for extensive conformational heterogeneity in a real chain. Quantitative comparisons of probability vectors generated from these simulations reveal the extent of conformational sampling in a simulation, enabling us to distinguish between situations in which protein regions are well-sampled, poorly sampled, or folded. To demonstrate the effectiveness of this approach, we apply our algorithm to several toy, synthetic, and biological systems. Accurately assessing local conformational sampling in simulations of IDRs will help better quantify new enhanced sampling methods, ensure force field comparisons are equivalent, and provide confidence that conclusions drawn from simulations are robust.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2897-2910"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555328","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":"Accelerated Hydration Site Localization and Thermodynamic Profiling.","authors":"Florian B Hinz, Matthew R Masters, Julia T Nguyen, Amr H Mahmoud, Markus A Lill","doi":"10.1021/acs.jcim.4c02349","DOIUrl":"10.1021/acs.jcim.4c02349","url":null,"abstract":"<p><p>Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein is critical for ligand recognition and binding. Therefore, identifying the location and thermodynamic properties of relevant water molecules is important for generating and optimizing lead compounds for affinity and selectivity for a given target. Computational methods have been developed to identify these hydration sites (HS), but are largely limited to simplified models that fail to capture multibody interactions or dynamics-based methods that rely on extensive sampling. Here, we present a method for fast and accurate localization and thermodynamic profiling of HS for protein structures. The method is based on a geometric deep neural network trained on a large, novel data set of explicit water molecular dynamics simulations. We confirm the accuracy and robustness of our model on experimental data and demonstrate its utility on several case studies.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2794-2805"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143527852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Blood-Brain Barrier Penetration Prediction by Machine Learning-Based Integration of Novel and Existing, In Silico and Experimental Molecular Parameters from a Standardized Database.","authors":"Clemens P Spielvogel, Natalie Schindler, Christian Schröder, Sarah Luise Stellnberger, Wolfgang Wadsak, Markus Mitterhauser, Laszlo Papp, Marcus Hacker, Verena Pichler, Chrysoula Vraka","doi":"10.1021/acs.jcim.4c02212","DOIUrl":"10.1021/acs.jcim.4c02212","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Predicting blood-brain barrier (BBB) penetration is crucial for developing central nervous system (CNS) drugs, representing a significant hurdle in successful clinical phase I studies. One of the most valuable properties for this prediction is the polar surface area (PSA). However, molecular structures are missing geometric optimization, which, together with lack of standardization, leads to variations in calculation. Additionally, prediction rules have been established by combining different molecular properties such as the BBB score or CNS multiparameter optimization (CNS MPO). This study aims to create an approach for 3D PSA calculation, to directly apply this value in combination with a set of 23 other parameters in a novel machine learning (ML)-based scoring, and to further evaluate existing prediction models using a standardized database. We developed and analyzed a standardized data set derived from the same laboratory, encompassing 24 calculated and experimentally determined molecular parameters such as PSA from various models, HPLC log &lt;i&gt;P&lt;/i&gt; values, and hydrogen bond characteristics for 154 radiolabeled molecules and licensed or well-characterized drugs. These molecules were classified into categories based on BBB penetration, nonpenetration, and interactions with efflux transporters. We supplemented these with a novel in silico 3D calculation of nonclassical PSA. Additionally, we have calculated published prediction rules based on this standardized and transparent database. Using these data, we trained various ML models within a 100-fold Monte Carlo cross-validation framework to derive a novel ML-based prediction score for BBB penetration and validated the three most used existing predictive rules. To interpret the influence of individual molecular parameters and different existing predictive rules, we employed explainable artificial intelligence methods including Shapley additive explanations (SHAP) and surrogate modeling. The ML approach outperformed existing scores for BBB penetration by applying a complex nonlinear integration of molecular properties, with the random forest model achieving the best performance for predicting binary BBB penetration (area under the receiver operating characteristic curve (AUC) 0.88, 95% confidence intervals: 0.87-0.90), and multiclass efflux transporter versus CNS-positive and CNS-negative prediction (AUC 0.82, 95% CI: 0.81-0.82). SHAP analysis revealed the multifactorial nature of the problem, highlighting the advantage of multivariate models over single predictive parameters. The ML model's superior predictive capability was demonstrated in comparison with existing scoring systems, like the CNS MPO (AUC 0.53), the CNS MPO Positron emission tomography (PET) (AUC 0.51), and BBB score (AUC 0.68) while also enabling the identification of efflux transporter substrates and inhibitors. Our integrated ML approach, combining experimental and in silico measurements with novel in silico methods based ","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2773-2784"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938273/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143555331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From NMR to AI: Do We Need ¹H NMR Experimental Spectra to Obtain High-Quality logD Prediction Models?","authors":"Arkadiusz Leniak, Wojciech Pietruś, Aleksandra Świderska, Rafał Kurczab","doi":"10.1021/acs.jcim.4c02145","DOIUrl":"10.1021/acs.jcim.4c02145","url":null,"abstract":"<p><p>This study presents a novel approach to ¹H NMR-based machine learning (ML) models for predicting logD using computer-generated ¹H NMR spectra. Building on our previous work, which integrated experimental ¹H NMR data, this study addresses key limitations associated with experimental measurements, such as sample stability, solvent variability, and extensive processing, by replacing them with fully computational workflows. Benchmarking across various density functional theory (DFT) functionals and basis sets highlighted their limitations, with DFT-based models showing relatively high RMSE values (average CHI logD of 1.12, lowest at 0.96) and extensive computational demands, limiting their usefulness for large-scale predictions. In contrast, models trained on predicted ¹H NMR spectra by NMRshiftDB2 and JEOL JASON achieved RMSE values as low as 0.76, compared to 0.88 for experimental spectra. Further analysis revealed that mixing experimental and predicted spectra did not enhance accuracy, underscoring the advantage of homogeneous datasets. Validation with external datasets confirmed the robustness of our models, showing comparable performance to commercial software like Instant JChem, thus underscoring the reliability of the proposed computational workflow. Additionally, using normalized RMSE (NRMSE) proved essential for consistent model evaluation across datasets with varying data scales. By eliminating the need for experimental input, this workflow offers a widely accessible, computationally efficient pipeline, setting a new standard for ML-driven chemical property predictions without experimental data constraints.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2924-2939"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143565643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MFF-nDA: A Computational Model for ncRNA-Disease Association Prediction Based on Multimodule Fusion.","authors":"Zhihao Guan, Xiu Jin, Xiaodan Zhang","doi":"10.1021/acs.jcim.5c00174","DOIUrl":"https://doi.org/10.1021/acs.jcim.5c00174","url":null,"abstract":"<p><p>Noncoding RNAs(ncRNAs), including piwi-interacting RNA(piRNA), long noncoding RNA(lncRNA), microRNA(miRNA), small nucleolar RNA(snoRNA), and circular RNA(circRNA), contribute significantly to gene expression regulation and serve as key factors in disease association studies and health-related exploration. Accurate prediction of ncRNA-disease associations is crucial for elucidating disease mechanisms and advancing therapeutic development. Recently, computational models based on a graph neural network have extensively emerged for identifying associations among various ncRNAs and diseases. However, existing computational models have not fully utilized integrative information on ncRNs and diseases, and reliance on GNN-based models alone may be limited in performance due to oversmoothing issues. On the other hand, existing models are mainly targeted at a specific type of ncRNA and may not be applicable to most ncRNAs. Therefore, to overcome these limitations, we propound a computational model MFF-nDA based on multimodule fusion. Specifically, we first introduce five types of similarity network information, including three types of ncRNA and two types of disease similarity information, in order to fully explore and optimize the multisource feature information on these entities. Subsequently, we establish three modules: heterogeneous network representation module based on Transformer, association network representation module based on graph convolutional network (GCN), and topological structure representation module based on graph attention network (GAT), which capture diverse features of nodes in heterogeneous networks and topological structure information reflected in association networks. The complementary effects of the three modules also help relieve the oversmoothing issue to some extent. By leveraging the multimodule fusion learning to comprehensively capture the diverse features of these entities, our model outperforms the available state-of-the-art methods, achieving an AUC greater than 0.9000 for each dataset. This demonstrates the highest predictive performance, making it a valuable tool for identifying potential ncRNA associated with diseases. The code of MFF-nDA can be accessed at https://github.com/Jack-Cxy/MFF-nDA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699096","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":"Locating Multiple Transition Pathways for Complex Biomolecules.","authors":"Kun Xi, Jinchu Liu, Lizhe Zhu","doi":"10.1021/acs.jcim.4c01604","DOIUrl":"10.1021/acs.jcim.4c01604","url":null,"abstract":"<p><p>Locating the low free energy paths (LFEPs) connecting different conformational states is among the major tasks for the simulations of complex biomolecules as the pathways encode the physical essence and, therefore, the underlying mechanism for their functional dynamics. Finding the LFEPs is yet challenging due to the numerous degrees of freedom of the molecules and expensive force calculations. To alleviate this issue, we have previously introduced a Traveling-Salesman-based Automated Path Searching (TAPS) approach that requires minimal input information to locate the LFEP closest to a given initial guess path. Despite its high efficiency for large biomolecules, it remains, as all path-searching methods, incapable of revealing multiple parallel LFEPs simultaneously, which are, however, near-ubiquitous. This work describes a comprehensive protocol that offers parallel LFEPs efficiently. Our protocol starts with a modified version of the parallel cascade approach, which extensively searches for a large pile of geometrically distinct paths of the target molecule in implicit solvents. These paths are clustered and then filtered by their cumulative barriers, yielding a smaller set of initial paths for subsequent optimization by TAPS in explicit solvents. Through this protocol, we successfully sampled eight LFEPs for the transition of Met-enkephalin from its 3₁₀-helix to the β-turn form, whose highest barriers range from 4.57 to 14.72 <i>k</i>_B<i>T</i>. Remarkably, for the activation of the L99A variant of T4 Lysozyme (T4L-L99A), our approach revealed four LFEPs. Among them, the dominant and second preferable paths (barrier of 11.8 and 19.2 <i>k</i>_B<i>T</i>) resemble previously reported mechanisms, while the other two (barrier of 23.7 and 25.3 <i>k</i>_B<i>T</i>) offer novel mechanistic insights of the flipping of residues M102/M106 and anticlock flipping of F114. These results demonstrate our protocol's robustness and efficiency in providing multiple transition paths for complex conformational changes of biomolecules.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"2961-2973"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595803","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, Shudong Wang, Kuijie Zhang, Hengxiao Li, Sibo Qiao, Yuanyuan Zhang, Shanchen Pang","doi":"10.1021/acs.jcim.4c02114","DOIUrl":"10.1021/acs.jcim.4c02114","url":null,"abstract":"<p><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":" ","pages":"3048-3063"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603046","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}