Journal of Chemical Information and Modeling 最新文献

{"title":"QSAR as a Small-Deformation Theory of Effective Free-Energy Landscapes","authors":"Jun-ichi Okada,&nbsp;Katsuhito Fujiu","doi":"10.1021/acs.jcim.6c01230","DOIUrl":"10.1021/acs.jcim.6c01230","url":null,"abstract":"<p>Quantitative structure–activity relationships(QSARs) remainwidely used in medicinal chemistry, yet their physical interpretationis often limited and the meaning of their applicability domain (AD)is commonly treated in statistical or descriptor-space terms. Herewe propose a conceptual framework in which QSAR is interpreted asa small-deformation theory of effective free-energy landscapes. Ligandbinding or chemical substitution is represented as a perturbationof an effective free-energy landscape defined over reduced state variables,and the resulting free-energy response is expressed as a contractedobservable over the equilibrium ensemble of the reference system viaa Zwanzig-type perturbation formula. At the phenomenological level,this response reduces to a low-order expansion in descriptor space,from which conventional linear QSAR emerges as the leading-order approximation.Within the assumed effective description and under stated regularityconditions, the corresponding first-order coefficients are given exactlyby the ensemble-averaged sensitivities of the free-energy landscapeto descriptor perturbations, without requiring a cumulant truncation.The AD acquires a physical meaning as the region in which a locallow-order expansion of the free-energy response remains valid. Inthe small-deformation regime, this same region further correspondsto ligand-induced landscape perturbations that remain local, smooth,and sufficiently weak over the reference ensemble. Conversely, insystems for which an effective-landscape description is appropriate,QSAR breakdown may be associated with phenomena such as activity cliffs,induced fit, and state-dependent binding, which can involve qualitativereorganization or rapid reweighting of the effective landscape. Fora specified thermodynamic system, the second-cumulant term providesa non-positive variance correction to the leading-order ensemble-averagefree-energy response, arising from the variance of the landscape perturbationover the reference ensemble. This correction reflects a fluctuation-assistedeffect in which configurational heterogeneity within the pre-existingensemble allows Boltzmann reweighting to redistribute statisticalweight toward microstates more strongly influenced by the perturbation.Independently, the exact free-energy response is no greater than theleading-order ensemble-average estimate, consistent with Jensen’sinequality. The present framework is conceptual. Its interpretationof QSAR coefficients as ensemble-averaged sensitivities of the free-energylandscape requires additional assumptions and state-resolved informationbeyond conventional compound-level QSAR data, and it does not by itselfprovide a prospective AD method.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12576–12586"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148933409","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":"Assessing the Generalizability of Machine Learning and Physics-Based Methods with DNA-Encoded Libraries","authors":"Marissa Dolorfino,&nbsp;Daniel Santos Perez,&nbsp;Yao Fu,&nbsp;Shu-Hang Lin,&nbsp;Sean McCarty,&nbsp;Matthew J. O’Meara,&nbsp;Terra Sztain","doi":"10.1021/acs.jcim.6c01611","DOIUrl":"10.1021/acs.jcim.6c01611","url":null,"abstract":"<p>Predicting protein–ligand binding is a centralchallengein computational drug discovery, and while machine learning (ML) andcofolding methods have advanced rapidly, their ability to generalizebeyond training or parametrization regimes remains insufficientlyunderstood. DNA-encoded libraries (DELs) enable ultralarge screeningof billions of molecules simultaneously, providing a useful testbedfor evaluating these approaches at scale. A recent NeurIPS competitionrevealed that even top-performing ML models trained on DEL data failedat generalizing to out-of-distribution (OOD) chemical space. We investigatedwhether integrating structural modeling could bridge this generalizationgap. We systematically assessed state-of-the-art ML, docking, andcofolding methods, including Schrödinger Glide, Rosetta GALigandDock,and Boltz-2 with three biologically diverse protein targets screenedagainst libraries containing multiple DEL synthesis formats. WhileML excels in-distribution, OOD hit discrimination is dependent onboth the target and ligand context, with no single method consistentlydominating. These findings demonstrate that benchmark performancealone is insufficient to predict OOD performance, highlighting theneed for system-dependent evaluation of binding prediction methods.We provide an open-source package for assessing protein–ligandprediction methods and analyzing high-throughput screening data: DEL-iver.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12360–12374"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003492","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":"REAPS: An All-Atom Receptor-Aware Geometric Deep Learning Framework for De Novo Design of Linear and Macrocyclic Peptide Binders","authors":"Yongkang Qiu,&nbsp;Jingxin Qiao,&nbsp;Lengjing Zhu,&nbsp;Le Du,&nbsp;Shengyong Yang,&nbsp;Jun Zou","doi":"10.1021/acs.jcim.6c02057","DOIUrl":"10.1021/acs.jcim.6c02057","url":null,"abstract":"<p>Peptide therapeutics occupy a critical niche betweensmall moleculesand biologics, yet the sequence design of peptide binders remainschallenging because their bioactive conformations and interfacialfitness are dictated by the receptor microenvironment. Current computationalapproaches largely treat this as a generic inverse folding problemthat relies on backbone geometry alone, overlooking the detailed physicochemicalconstraints of the receptor pocket. To bridge this gap, we introducethe REceptor-Aware Peptide Sequence Designer (REAPS), a geometricgraph neural network that reframes sequence design for peptide bindersby treating the receptor as a fully observable all-atom context. Multidimensionalevaluations show that REAPS outperforms the widely used inverse foldingmodel ProteinMPNN in sequence recovery, structural fidelity, and interface-levelbiophysical metrics across both linear and macrocyclic peptide binders.We further integrate REAPS with structural hallucination in a closed-loop <em>de novo</em> discovery pipeline, yielding peptide candidateswith experimentally verified functional activity at the neurokinin-3receptor (NK3R). The source code, model checkpoints, processed datasets, preprocessing scripts, and an example design workflow are publiclyavailable at https://github.com/Mistletoe-git/REAPS.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12477–12491"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149003495","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":"Surfactant-Mediated Enantioselective Extraction: Interfacial Behavior Investigation from a Microscopic Perspective","authors":"Yunna Xue,&nbsp;Xinggui Zhou,&nbsp;Xiangyang Zhang","doi":"10.1021/acs.jcim.6c01740","DOIUrl":"10.1021/acs.jcim.6c01740","url":null,"abstract":"<p>Enantioselective liquid–liquid extraction (ELLE)based oncyclodextrin host–guest recognition is a promising green strategyfor chiral resolution. However, its industrial application is oftenhindered by restricted chiral recognition efficiency and ill-definedinterfacial mass transfer mechanisms. In this work, a multiscale approach─integratingmacro-equilibrium experiments, molecular dynamics simulations, andquantum chemical calculations─was employed to decode the interfacialmolecular interactions and their decisive roles in the ELLE of ibuprofen.The interface was regulated and reconstructed through the introductionof surfactants. The anionic surfactant SDS shortened extraction equilibriumtime by approximately 30%, while the facial amphiphilic NaDC extendedequilibration but improved the enantiomeric excess. MD simulationsrevealed that the addition of SDS and NaDC increased the interfacialthickness from 0.41 nm by 1.23 and 1.51 times, respectively. Via umbrellasampling, the chiral migration process exhibited characteristic free-energychanges of approximately −11.48 kJ·mol^–1 and +35.58 kJ·mol^–1 when transferring fromthe organic and aqueous phases to the interface, respectively. Furthermore,quantum chemical analysis quantified the interaction energy differences,demonstrating that steric hindrance and hydrogen-bonding intensityvariations drove the observed changes in equilibrium parameters. Thesefindings establish a conceptual microscopic model for interface-mediatedchiral partitioning, providing a robust theoretical framework foroptimizing separation efficiency and process intensification in ELLEsystems.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12682–12693"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149021788","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 Chemical Foundation Model Benchmark for PFAS Bioactivity Prediction: Multi-Task Fine-Tuning Beats Fingerprint Baselines, PFAS Domain Pretraining Does Not Help","authors":"Yue Zhu,&nbsp;Qingyang Liu","doi":"10.1021/acs.jcim.6c02202","DOIUrl":"10.1021/acs.jcim.6c02202","url":null,"abstract":"<p>Per- and polyfluoroalkyl substances (PFAS) comprise morethan 21,000chemicals, yet fewer than 1.3% have any experimental bioactivity measurement,making structure-based hazard prediction the gating problem for prioritizingthe untested majority. Existing PFAS models are single-task and rarelyevaluated under leakage-aware cross-validation. We present the firstmultitask chemical foundation-model benchmark for PFAS bioactivity,fine-tuning off-the-shelf MolFormer-XL with 156 ToxCast classificationheads under Tanimoto-Butina cluster-out cross-validation, which isappropriate because 89% of PFAS have empty Murcko scaffolds. The modelreaches macro AUROC 0.764, beating a Morgan-fingerprint XGBoost baselineby +0.18 and winning on 155 of 156 end points. That baseline is, however,partly degenerate: binary Morgan fingerprints map a quarter of thecompounds onto identical vectors, and sweeping eight 1<em>D</em>/2D/3D representations on the same folds raises the best classicalmodel to 0.688, so the margin properly attributable to the foundationmodel is +0.076. A matched factorial ablation shows that backbonefine-tuning and multitask learning are synergistic: multitask learninghelps only once the backbone is fine-tuned (+0.08). This interactionis backbone- and task-dependent: it reproduces on Tox21 for two backbonesbut is absent for ChemBERTa-2 on PFAS, and label-free encoder diagnosticsshow this is not because ChemBERTa-2 represents PFAS poorly, so staticrepresentation quality does not predict postfine-tuning performance.PFAS-specific domain-adaptation pretraining gives no detectable improvement(Δ = – 0.01 to – 0.03). Mondrian-by-class conformalcalibration improves rare-active-class coverage from 0.36 to 0.78,and an applicability-domain analysis shows only 26% of untested PFASlie close enough to the training set for the model to be applied withoutcaveat. The representation also transfers to soil-water partitioning(<em>K</em>_d) regression, where it holds at Spearmanρ = 0.75 even on PFAS chemistries absent from training whilethe fingerprint baselines collapse.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12518–12533"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049660","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":"MultiRankDTA: A Hybrid Ranking Framework for Robust and Generalizable Drug-Target Affinity Prediction","authors":"Qiang Huang,&nbsp;Yifan Wu,&nbsp;Lin Wang,&nbsp;Jianhui Lin,&nbsp;Yiming Li,&nbsp;Min Zeng,&nbsp;Min Li","doi":"10.1021/acs.jcim.6c00799","DOIUrl":"10.1021/acs.jcim.6c00799","url":null,"abstract":"<p>Predicting drug-target affinity (DTA)is essential forscreeningcandidate compounds in early-stage drug discovery. However, most existingcomputational methods formulate DTA prediction as a regression task,leading to an objective mismatch with the practical need to rank andprioritize candidate compounds based on their relative binding strengths.Moreover, existing ranking-based methods often adopt a fixed rankingobjective or scoring function, which may limit their flexibility whenthey are applied to diverse drug-target interaction scenarios. Toaddress these challenges, we propose MultiRankDTA, a novel frameworkthat formulates DTA prediction as a multilevel ranking task to betteralign with the practical goal of drug screening. By integrating pointwise,pairwise, and listwise ranking strategies through a mixture-of-experts(MoE) module, MultiRankDTA enables dynamic and adaptive compound prioritization.Furthermore, we introduce a fusion network to reduce data set-specificbiases by leveraging auxiliary supervision from multiple sources.Extensive experiments demonstrate that MultiRankDTA achieves superiorperformance compared to state-of-the-art computational methods acrossmultiple benchmark data sets and delivers strong results on an independentbenchmark for virtual screening, indicating its potential for generalizationand practical utility. The source code can be obtained at https://github.com/CSUBioGroup/MultiRankDTA.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12291–12304"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149092246","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":"Peptide Assembly Design Algorithm: A User-Friendly Computational Tool for Discovering Amyloid-forming Peptides","authors":"Haoyu Wang,Sudeep Sarma,Carol K. Hall","doi":"10.1021/acs.jcim.6c01893","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c01893","url":null,"abstract":"Rational design of self-assembling peptides that form amyloid-like structures is challenging due to the vast combinatorial sequence space for amino acids. To address this challenge, we have developed the Peptide Assembly Design (PepAD) algorithm, a Monte Carlo-based method for designing self-assembling peptides based on pre-defined amyloid backbones. In this work, we introduce the user-friendly package, PepAD, to the research community (GitHub link: https://github.com/CarolHall-NCSU-CBE/PepAD-User-Friendly-Package). This package allows users to flexibly design and customize peptides by specifying initial structure, amino acid composition, and constraints. Detailed instructions for code compilation, execution, and interpretation of outputs are provided. Guidance on selecting several important design parameters is provided. As an example to illustrate the use of PepAD, we describe the design of peptides that assemble into antiparallel fibrils with and without single-site constraints. We expect that this release will streamline the design process and speed up the discovery of programmable self-assembling peptides for biofunctional materials.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"10 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149152069","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":"ChemLint: Conversational Cheminformatics with Large Language Models","authors":"Derek van Tilborg,&nbsp;Francesca Grisoni","doi":"10.1021/acs.jcim.6c00638","DOIUrl":"10.1021/acs.jcim.6c00638","url":null,"abstract":"<p>We introduce ChemLint, an open-source Model Context Protocol(MCP)server that connects any MCP-compatible large language model to acurated suite of local cheminformatics and machine learning tools,enabling rigorous molecular data handling through a conversationalinterface. Molecular machine learning studies are frequently underminedby inconsistent data preprocessing, including invalid SMILES, unresolvedduplicates, and train-test leakage, yet existing large language model(LLM)-based chemistry tools do not address these data-centric challenges.ChemLint provides tools for data exploration and diagnostics, moleculestandardization, and machine learning modeling. All operations areexecuted deterministically by established libraries and recorded ina project manifest that traces every action, supporting reproducibilityand making curation choices explicit. We demonstrate through severalexamples how ChemLint can be used to identify common data qualityissues, evaluate splitting strategies, and execute complete modelingpipelines from raw data to evaluation. ChemLint is freely availableat https://github.com/molML/ChemLint.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12568–12575"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822267","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":"AI Accelerated Chemical Screening Integrates ChemBERTa to Identify Repositionable CASP4 Inhibitors via MD Simulations and MM/PBSA Analysis","authors":"Mubashir Hassan,&nbsp;Sidra Ghayour Bhatti,&nbsp;Muhammad Yasir,&nbsp;Wanjoo Chun,&nbsp;Andrzej Kloczkowski","doi":"10.1021/acs.jcim.6c02284","DOIUrl":"10.1021/acs.jcim.6c02284","url":null,"abstract":"<p>This study employed an integrated ligand-based virtualscreeningpipeline to identify potential CASP4 inhibitors from the DrugBankdatabase, leveraging docking-score prioritization, SMILES-derivedChemBERTa embeddings, and key physicochemical descriptors. Buildingon this foundation, the workflow incorporated virtual screening, cheminformaticsmodeling, PK–PD evaluation, molecular docking, molecular dynamicssimulations, and MM/PBSA free-energy analysis to systematically prioritizerepurposed DrugBank compounds for CASP4-targeted Alzheimer’sdisease therapy. A Random Forest classifier trained on the hybridChemBERTa physicochemical feature set distinguished active from inactivecompounds with ∼95% accuracy (ROC–AUC = 0.73) and achieveda ∼3.5-fold enrichment of active compounds among the top-rankedhits, while a companion Random Forest regressor, trained on the experimentalpIC₅₀ values of active compounds, ranked candidates bypredicted potency. In addition, integrated cheminformatics modeling,PK–PD analysis, and molecular docking further narrowed theselection to the top five candidate compounds: DB00519, DB01068, DB06202,DB08882, and DB05316. Finally, MD simulations and MM/PBSA calculationsestablished clear thermodynamic support for DB05316 and DB00519, whosebinding free energies (−21.4 and −20.9 kcal/mol, respectively)exceeded even the reference compound donepezil, highlighting themas the most promising CASP4 inhibitors. Overall, this workflow providesan efficient and robust strategy for prioritizing repositioned DrugBankcompounds as potential CASP4 inhibitors against Alzheimer’sdisease.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12740–12771"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148973934","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":"In-Context Molecular Property Prediction with LLMs: A Blinding Study on Memorization and Knowledge Conflicts","authors":"Matthias Busch,&nbsp;Marius Tacke,&nbsp;Sviatlana V. Lamaka,&nbsp;Mikhail L. Zheludkevich,&nbsp;Christian J. Cyron,&nbsp;Christian Feiler,&nbsp;Roland C. Aydin","doi":"10.1021/acs.jcim.6c00961","DOIUrl":"10.1021/acs.jcim.6c00961","url":null,"abstract":"<p>The capabilities of large language models (LLMs) haveexpandedbeyond natural language processing to scientific prediction tasks,including molecular property prediction. However, their effectivenessin in-context learning remains ambiguous, particularly given the potentialfor training data contamination in widely used benchmarks. This paperinvestigates whether LLMs perform genuine in-context regression onmolecular properties or instead rely on verbatim retrieval of memorizedtarget values. Furthermore, we analyze the interplay between pretrainedknowledge and in-context information through a series of progressivelyblinded experiments. We evaluate nine LLM variants across three families(GPT-4.1, GPT-5, Gemini 2.5) on three MoleculeNet data sets (Delaneysolubility, Lipophilicity, QM7 atomization energy) using a systematicblinding approach that iteratively reduces available information,complemented by 0-, 60-, and 1000-shot in-context sample sizes asan additional control for information access. To validate the memorizationanalysis and the blinding experiments, we add a positive and a negativecontrol for the memorization experiments and structural referencebaselines for the multishot experiments, as well as bootstrap confidenceintervals for all results. We find no evidence of verbatim retrievalon the legacy benchmarks and show that blinding exposes conflictsbetween pretrained knowledge and in-context information. This workprovides a principled framework for evaluating molecular propertyprediction under controlled information access.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 19","pages":"12305–12315"},"PeriodicalIF":6.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985072","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}