Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

{"title":"Toward Predictive Modeling of Layered-Oxide Cathodes in Na-Ion Batteries","authors":"Arianna Massaro,&nbsp;Aniello Langella,&nbsp;Francesca Fasulo,&nbsp;Adriana Pecoraro,&nbsp;Ana B. Muñoz-García,&nbsp;Michele Pavone","doi":"10.1002/wcms.70090","DOIUrl":"https://doi.org/10.1002/wcms.70090","url":null,"abstract":"<div>\u0000 \u0000 <p>Layered Na_xTMO₂ transition-metal oxides are among the most promising cathodes for Na-ion batteries, yet their predictive computational modeling remains intrinsically challenging. Their electrochemical behavior emerges from a delicate interplay between multivalent redox chemistry, structural flexibility, configurational disorder, and kinetic evolution during cycling. In this perspective, we provide a methodology-centered overview of the main theoretical challenges associated with modeling these materials and discuss practical strategies to bridge electronic accuracy and structural/chemical complexity. We first examine the capabilities and limitations of density functional theory (DFT) in describing cationic and anionic redox processes, highlighting the role of self-interaction error and the complementary use of DFT+U and hybrid functionals. We then address the explicit treatment of configurational disorder, emphasizing special quasi-random structures (SQS) and antisite defect modeling as essential tools to capture entropy-driven stabilization in realistic local environments. Finally, we discuss the interplay between thermodynamics and kinetics in Na intercalation, connecting voltage calculations, ion diffusion barriers and phase-transition pathways described via NEB methods. Rather than advocating a single best technique, we argue that predictive modeling of Na-ion cathodes requires integrated multiscale frameworks in which electronic-structure methods, statistical sampling, and emerging machine-learning potentials operate synergistically. In this evolving landscape, DFT-based first-principles calculations remain the indispensable foundation for transforming atomistic insights into rational materials discovery.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 6","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149311272","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 Perspective on Advancing SAS-Restrained MD for Lipid Membranes","authors":"Diane Lima,&nbsp;Daniel L. Z. Caetano,&nbsp;Luiz F. C. Rodrigues,&nbsp;Vinicius F. dos Santos,&nbsp;Michele Cascella,&nbsp;Rosangela Itri,&nbsp;Thereza A. Soares","doi":"10.1002/wcms.70089","DOIUrl":"https://doi.org/10.1002/wcms.70089","url":null,"abstract":"<p>Small-angle X-ray and neutron scattering (SAXS/SANS), combined with molecular dynamics (MD) simulations, have matured into a quantitative framework for refining protein structural ensembles, supported by methodologies spanning ensemble selection, maximum-entropy reweighting, Bayesian inference, and replica-averaged restrained simulations. Their extension to lipid membranes has lagged considerably. In this Perspective, we argue that this lag is conceptual rather than algorithmic: protein scattering reports on finite particles with a rich structural information content, whereas membrane scattering probes a quasi-two-dimensional fluctuating interface governed by collective elastic modes, with only a handful of independent structural channels accessible from the one-dimensional bilayer form factor. We review the inference frameworks underpinning protein SAS–MD integration, examine the theoretical and experimental foundations of membrane scattering, and analyze model-based and Hamiltonian-level restrained-ensemble approaches for bilayers. Six methodological directions are identified—spanning forward modeling, uncertainty quantification, multi-observable integration, collective-mode sampling, field-based inference, and machine-learning surrogates—that together define a tractable agenda for placing membrane ensemble refinement on the same statistical footing now established for proteins.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70089","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149258052","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":"The Algorithm Question: When Architectural Complexity Meets Biological Reality in Antimicrobial Peptide Prediction","authors":"Xingwen Long,&nbsp;Yikun Lin,&nbsp;Jiajing Xie,&nbsp;Jingyan Wang,&nbsp;Xuan Wei,&nbsp;Lifang Wei,&nbsp;Zixin Deng,&nbsp;Jiangtao Gao","doi":"10.1002/wcms.70087","DOIUrl":"https://doi.org/10.1002/wcms.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>Computational prediction and design of antimicrobial peptides (AMPs) has progressed quickly, moving from physicochemical descriptors paired with classical machine learning to transformer architectures and generative models. Each new architecture claims improved performance, yet basic questions remain unanswered. Does added complexity produce proportionally better biological predictions, and what do different architectures actually learn? Recent panoramic surveys have cataloged progress; the present review takes a different stance, arguing that the field has favored architectural novelty over biological understanding and has widened the gap between computational metrics and therapeutic relevance. Three findings emerge from a re-examination of the evidence. First, deep learning models do not consistently outperform well-designed shallow approaches, and both encode statistically similar chemical information. Second, reported in vitro validation rates of 59% to 100% for heavily pre-screened designs cluster within a narrow band irrespective of algorithm class, which suggests that pre-screening pipelines, rather than raw model accuracy, drive these numbers. Third, generative models such as HydrAMP, Multi-CGAN, and latent-diffusion frameworks sample within the physicochemically constrained envelope defined by their training data, and because antimicrobial activity can change sharply with a single substitution, descriptor-space novelty does not by itself establish functional novelty. To support such a critique with original evidence, the validation-rate analysis is extended across 18 representative studies. Two community instruments are then proposed: a Biology-First Algorithm Selection (BFAS) decision framework that operationalizes a four-axis selection protocol over task, data, interpretability, and compute budget; and AMP-CASP, an AMP-equivalent of the CASP blind-benchmark that turns an earlier call for a CASP-like community assessment into an operational protocol, with held-out activity, toxicity, and stability labels, homology-aware splitting, organism-resolved endpoints, and explicit assay-condition metadata. Progress now depends on an honest assessment of what algorithms can and cannot deliver in moving from computational prediction to clinical translation.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149047919","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":"EGNN-Based Generative Models for 3D Molecular Generation","authors":"Siliang Chen,&nbsp;Daihan Wang,&nbsp;Zhaoping Pan,&nbsp;Guobo Li,&nbsp;Gu He","doi":"10.1002/wcms.70086","DOIUrl":"https://doi.org/10.1002/wcms.70086","url":null,"abstract":"<div>\u0000 \u0000 <p>Equivariant graph neural networks (EGNNs) are becoming the geometric infrastructure of 3D molecular generation for AI-aided drug discovery. By enforcing E(n), E(3), or SE(3) equivariance, they separate physical molecular structure from arbitrary coordinate-frame conventions and ensure that predicted coordinates, denoising directions, and velocity fields transform consistently with molecular geometry. This symmetry-aware design supports direct modeling of conformations and protein–ligand spatial relationships while remaining compatible with diffusion, flow-based, autoregressive, and hybrid generative frameworks. Consequently, EGNNs enable more geometrically consistent generation and facilitate controllable design conditioned on binding pockets, pharmacophores, fragments, scaffolds, reference ligands, or molecular properties. Their benefits, however, are bounded by what equivariance encodes. Coordinate-frame consistency does not itself enforce valid bonds, valence, stereochemistry, topology–geometry agreement, synthesizability, or biological activity. Moreover, practical models must reconcile discrete chemical variables with continuous geometric dynamics, represent long-range interactions without prohibitive computational cost, and account for protein flexibility, solvent, metal ions, and induced fit. Performance is further constrained by biased docking or property predictors, heterogeneous benchmarks, and limited prospective validation. This review synthesizes how EGNNs function across major generative paradigms and argues that future drug-oriented systems should combine joint 2D–3D graph generation, physically credible interaction modeling, synthesis and ADMET constraints, multi-objective optimization, and closed-loop experimental feedback. EGNNs should therefore be viewed not as a complete chemical solution, but as the symmetry-aware foundation upon which more testable and biologically relevant molecular design systems can be built.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148942322","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 for Protein Modeling: From Single Structure to Bound Complex and Thermodynamic Ensemble, With a Focus on Architectural Design","authors":"Daniele Angioletti,&nbsp;Matteo Carli,&nbsp;Marco Nobile,&nbsp;Vittorio Limongelli","doi":"10.1002/wcms.70082","DOIUrl":"https://doi.org/10.1002/wcms.70082","url":null,"abstract":"<p>We survey modern deep-learning approaches to protein conformational modeling through the lens of architectural design. We organize the literature into three increasingly expressive paradigms: (I) single-structure prediction, (II) prediction of molecular binding complexes, and (III) conformational ensemble generation. For each paradigm, we outline a representative set of models to sketch a practical taxonomy, and we summarize their key achievements, limitations, and common evaluation practices. Across the paradigms, we highlight recurring design choices that shape performance and generalization, including enforced SE(3) equivariance versus learned symmetry; MSA-driven coevolution versus protein language model priors; deterministic prediction versus generative sampling; explicit energetic supervision versus implicit learning; and integrative modeling across heterogeneous data modalities. While single-structure prediction is now relatively well established, comparable maturity has not yet been reached for binding-complex prediction and, especially, for generating faithful thermodynamic ensembles with reliable population weights, which remains an open challenge. We discuss open challenges in building physically grounded and transferable models, including data availability and fidelity, the choice of inductive biases to pursue generalization, and the need for rigorous model evaluation. Ultimately, we indicate generative kinetics as an aspirational frontier.</p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70082","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860004","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":"Machine Learning-Aided Small-Molecule Virtual Screening: Recent Advances and Future Perspectives","authors":"Yifei Wang,&nbsp;Nupur Bansal,&nbsp;Shiyun Wa,&nbsp;Simone Sciabola,&nbsp;Ye Wang","doi":"10.1002/wcms.70084","DOIUrl":"https://doi.org/10.1002/wcms.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>Virtual screening (VS) on small molecules aims to identify promising drug candidates against protein targets from expansive chemical libraries by balancing the core requirements of accurate scoring and efficient search against the inherent trade-off between accuracy and speed. This survey provides a comprehensive review of how Artificial Intelligence and Machine Learning (AI/ML) are redefining this landscape across three critical dimensions. First, we examine the evolution of AI-driven scoring functions, which utilize AI/ML models to capture complex structure–activity relationships from massive biochemical datasets, significantly enhancing structure- and ligand-based evaluations beyond traditional heuristics. Second, we summarize the emergence of efficient search algorithms that iteratively prioritize informative compounds to reduce search efforts by orders of magnitude. Third, we review the paradigm shift toward generative molecular design, making VS transition from screening fixed libraries to the <i>de novo</i> generation of molecules optimized for specific structural contexts and multi-objective properties. This review outlines the transition toward end-to-end, adaptive discovery systems that ensure computational hits are biologically potent, structurally optimized, and synthetically accessible.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860006","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 Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity","authors":"José Ferraz-Caetano,&nbsp;Filipe Teixeira,&nbsp;M. Natália D. S. Cordeiro","doi":"10.1002/wcms.70085","DOIUrl":"https://doi.org/10.1002/wcms.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern chemistry is pushing the limits of traditional Artificial Intelligence (AI) models, placing unprecedented demands on data availability to address humanity's most pressing challenges. One particular concern is AI's dependence on large, curated data and its tendency to deviate from or misrepresent fundamental chemistry principles. Nonetheless, this concern is often overshadowed by the urgent demand for emergent solutions to real-world problems. This perspective describes the incorporation of a domain-specific knowledge representation &amp; reasoning (KR&amp;R) framework with machine learning (ML) for predictive chemistry. KR&amp;R is presented as a framework to represent chemical knowledge, making a formal connection between inductive hypothesis generation and deductive reasoning. By integrating scientific rules into data-driven processes, upholding a “chemist in the loop” approach, KR&amp;R ensures that ML models are understandable and consistent with existing chemical theory. These concepts are illustrated by case studies where KR&amp;R improves the interpretability of ML predictive models targeting thermodynamic properties (Δ<i>G</i>_sol, Δ_vap<i>H</i>_m°), reaction yields, and catalytic performance. These examples also show KR&amp;R's importance in managing the complexity of modern computational chemistry, establishing it as a key component of explainable AI in the field.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 5","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860005","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":"Integrative Computational Strategies for Dynamic GPCR Landscapes: From Conformational Mechanisms to Drug Design","authors":"Jieying Zang,&nbsp;Shihang Wang,&nbsp;Kai Xu,&nbsp;Xinke Zhan,&nbsp;Yanan Tian,&nbsp;Xiaojun Yao,&nbsp;Huanxiang Liu","doi":"10.1002/wcms.70083","DOIUrl":"https://doi.org/10.1002/wcms.70083","url":null,"abstract":"<div>\u0000 \u0000 <p>G protein-coupled receptors (GPCRs) are the largest superfamily of membrane proteins and remain one of the most important classes of therapeutic targets. Although structural biology has provided valuable static structures for drug discovery, the translation of these insights into effective therapeutic strategies remains challenging because GPCR function and regulation are governed by complex conformational dynamics, metastable-state transitions, and long-range allosteric coupling. This review examines how computational strategies, including molecular dynamics (MD) simulations, artificial intelligence (AI) methods, and their integration, are being used to bridge this gap across three major areas, namely structural landscape mapping, mechanistic elucidation, and drug discovery. First, computational approaches for defining receptor architecture are summarized, combining static structure prediction with dynamic refinement to map conformational ensembles. Then, binding-site discovery and dynamic characterization are discussed, along with the identification of metastable states and the reconstruction of free energy landscapes. Subsequently, the use of AI and MD in elucidating signaling mechanisms is examined. Particular attention is given to decoding ligand binding and dissociation, allosteric communication networks, and the structural basis of biased signaling and effector coupling. Finally, computational workflows for therapeutic development, including virtual screening and the optimization of lead compounds, are highlighted. Future directions for integrating AI and MD are discussed, with the goal of moving GPCR research from static structural description toward dynamic mechanistic understanding and next-generation therapeutic development.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 3-4","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148754017","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 Dynamic Theozyme Model: A New Strategy for the Study of Enzyme Catalysis","authors":"Xin Wang,&nbsp;Yunqing Gao,&nbsp;Yinghan Chen,&nbsp;Hongwei Chen,&nbsp;Xu Gao,&nbsp;K. N. Houk,&nbsp;Yong Liang","doi":"10.1002/wcms.70081","DOIUrl":"https://doi.org/10.1002/wcms.70081","url":null,"abstract":"<div>\u0000 \u0000 <p>The dynamic interactions between enzymes and substrates, as well as the accurate elucidation of enzymatic catalytic mechanisms, are key scientific questions in modern enzymology. The development of quantum chemical calculation methods provides a new technical means for the study of enzymatic catalytic mechanisms. Among these, the theozyme model can enormously simplify the required computations and has become an important method for exploring enzymatic catalysis. However, as catalytic reactions discovered grow increasingly complex, the theozyme model built from static structures is increasingly found to be inadequate for accurate explanations of catalytic mechanisms. We have now coupled molecular dynamics (MD) simulations with the theozyme model to create a dynamic theozyme model for the study of complex catalytic systems. The new strategy utilizes MD simulations of transition states or key intermediates to obtain structural information during catalysis, providing reliable guidance for constructing the theozyme model. We have applied this strategy to several distinct catalytic systems, successfully revealing catalytic mechanisms leading to regioselectivity and stereoselectivity. The strategy proposed herein effectively integrates dynamic interaction analysis with quantitative mechanism evaluation, providing a research paradigm that balances accuracy and efficiency for elucidating enzymatic catalytic mechanisms.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Biochemistry and Biophysics</li>\u0000 \u0000 <li>Structure and Mechanism &gt; Reaction Mechanisms and Catalysis</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 3-4","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753581","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 Shape of No Escape: Membrane Self-Organization Under Confinement","authors":"Luis Segundo Mayorga,&nbsp;Maria Laura Mascotti,&nbsp;Diego Masone","doi":"10.1002/wcms.70080","DOIUrl":"https://doi.org/10.1002/wcms.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>Spatial confinement radically changes the collective behavior of amphiphilic molecules, generating self-assembled entities that significantly exceed bulk systems in morphological and topological complexity. Under geometric persuasion (and a modest nudge from entropy), these molecules orchestrate unexpected architectures: vesicle-in-vesicle nesting, fenestrated structures, and convoluted hydrophilic/hydrophobic chambers with curious properties for intracellular transport. Remarkably, confined systems echo the cell's spatially restricted nature, depicting fusion, fission, and engulfment events, suggesting that confinement does not merely impose boundary constraints but natural design principles: tune the cage, and the molecules will improvise a new choreography. Within nanosciences, recognizing confinement as a controllable parameter extends the traditional concept of self-assembly from a passive chemical consequence into an engineerable route toward hierarchical 3D structures with programmable form and function, each one a little kingdom with its own rules for curvature and asymmetry.</p>\u0000 <p>This article is categorized under:\u0000\u0000 </p><ul>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Biochemistry and Biophysics</li>\u0000 \u0000 <li>Structure and Mechanism &gt; Computational Materials Science</li>\u0000 \u0000 <li>Molecular and Statistical Mechanics &gt; Molecular Dynamics and Monte-Carlo Methods</li>\u0000 </ul>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 3-4","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753114","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}