Wiley Interdisciplinary Reviews: Computational Molecular Science最新文献

{"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}

{"title":"RINRUS: A Toolkit for the Construction of Reproducible and Reliable QM-Cluster Models of Enzyme Active Sites","authors":"Dominique A. Wappett,&nbsp;Qianyi Cheng,&nbsp;Thomas J. Summers,&nbsp;Taylor J. Santaloci,&nbsp;Donatus A. Agbaglo,&nbsp;Tejaskumar A. Suhagia,&nbsp;Manuel Palma Banos,&nbsp;Nathan J. DeYonker","doi":"10.1002/wcms.70078","DOIUrl":"https://doi.org/10.1002/wcms.70078","url":null,"abstract":"<div>\u0000 \u0000 <p>Effectively constructing an enzyme active site model is not only the most important task involved in quantum mechanical (QM) modeling of enzymes; it is also one of the most difficult. Without widely agreed-upon standards for selecting, truncating, and constraining residues, QM-cluster models have traditionally been built by bespoke approaches that are rarely consistent between labs. The Residue Interaction Network ResidUe Selector (<i>RINRUS</i>) is a software toolkit that automates and standardizes QM-cluster model construction with clear rules guaranteeing predictability and reproducibility. By selecting active site residues with interatomic contact networks, <i>RINRUS</i> replaces tedious visual inspection and chemical intuition with a data-driven approach. Algorithmic protocols are used for structure trimming, capping, and constraining to ensure the protein is truncated in chemically sensible places while keeping the system size reasonable. <i>RINRUS</i> can also prepare QM input files for the Gaussian, ORCA, <span>Q-Chem</span>, and <span>Psi4</span> software packages. This fast and user-friendly automation of QM-cluster model design and input file preparation eliminates many of the learning barriers, hidden labor costs of project design/implementation, and inconsistencies that have historically plagued QM-cluster modeling. <i>RINRUS</i> is open-source and available for download at https://github.com/natedey/RINRUS.</p>\u0000 </div>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 3-4","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534253","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":"Mixed Quantum–Classical Dynamics for Molecular Excited States: Reliability, Scalability, and Representation","authors":"Rafael S. Mattos,&nbsp;Mario Barbatti","doi":"10.1002/wcms.70079","DOIUrl":"https://doi.org/10.1002/wcms.70079","url":null,"abstract":"<p>Mixed quantum–classical dynamics (MQCD) enables nonadiabatic simulations of molecular excited states by combining classical nuclear trajectories with quantum electronic-state population flow. The field is moving beyond benchmark small molecules toward nanoscopic systems, active environments, and long timescales—regimes where MQCD is stretched by three coupled demands: reliability, scalability, and flexibility in the electronic representation set by decoherence. Reliability means getting the controlling physics right: initial ensembles consistent with the experimental preparation, accurate potential energies and couplings driving the system's evolution, and correct nonadiabatic branching and recombination. Scalability demands reducing time-to-converged observables by curbing repeated high-level electronic-structure calls. Surrogate excited-state models (including machine-learning potentials) can replace much of the on-the-fly electronic structure, while active learning, multi-fidelity strategies, and uncertainty quantification guide where new reference data and additional sampling are actually needed. Flexibility of representation is essential in dense excited-state manifolds, where the decoherence-selected pointer basis may differ from the adiabatic energy basis; localized representations and open-quantum-system tools (Lindblad dynamics, stochastic unraveling) provide practical routes to encode that choice. Together, these directions define an agenda for MQCD that targets larger, longer, and more environmentally complex excited-state dynamics while retaining a trajectory-based workflow.</p><p>This article is categorized under:\u0000\u0000 </p>","PeriodicalId":236,"journal":{"name":"Wiley Interdisciplinary Reviews: Computational Molecular Science","volume":"16 3-4","pages":""},"PeriodicalIF":10.9,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.70079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148534251","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}