Journal of Chemical Information and Modeling 最新文献

{"title":"Deep-Learning Predictions of Biomolecular Structures: Persistent Limitations and New Horizons Extended by Explicit Ion Addition","authors":"Jules Marien,Sujith Sritharan,Beatrice Caviglia,Raphaelle Versini,Lucas Auclair,Pierre Barraud,Carine Tisné,Alaa Reguei,Samuel Murail,Fabrice Leclerc,Elise Duboué Dijon,JianJun Tao,Nathalie Basdevant,Marc Baaden,Chantal Prévost,Sophie Sacquin-Mora,Antoine Taly","doi":"10.1021/acs.jcim.6c02555","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c02555","url":null,"abstract":"The advent of deep-learning-driven tools such as AlphaFold has revolutionized the prediction of biomolecular structures, offering unprecedented accuracy and accessibility for proteins, RNA, and their complexes. While these tools have demonstrated remarkable success in benchmarking competitions and enabled experimentalists to generate models with ease, their widespread use has also highlighted persistent challenges. These include difficulties in assessing model confidence, limitations in predicting transmembrane domains, nucleic acids, conformational diversity, and interactions with ions or ligands, as well as the tendency to misfold intrinsically disordered regions (IDRs). In this perspective, we critically evaluate the strengths and limitations of current AI-based structure prediction tools through illustrative examples with a particular emphasis on the impact of explicit ion modeling. We notably report that the explicit addition of a few potassium cations to the prediction of IDRs or G-quadruplexes can substantially shift AF3’s predicted conformation compared to “dry” predictions. On this basis, we suggest modeling sequences both “dry” and in the presence of explicit potassium cations as a simple, practical way to sample alternative conformations and to expose disordered regions that current predictors tend to overfold. We discuss the importance of reporting confidence metrics in publications to avoid overinterpretation. Furthermore, we address the unique challenges of RNA structure prediction, where data scarcity and structural complexity limit the performance of both classical and deep learning methods. Our analysis underscores the need for continued methodological advancements, integration of complementary computational tools, and expansion of high-quality experimental data sets.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"16 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374739","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":"Proteome-Wide Identification and Membrane Positioning of Single-Pass Membrane Proteins Using AlphaFold Models","authors":"Zigang Song,L. Ponoop Patro,Alexey Kovalenko,Égide Ntwari,Yelnaz Rysbek,Irina Pogozheva,Andrei Lomize,Wonpil Im","doi":"10.1021/acs.jcim.6c03090","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c03090","url":null,"abstract":"Single-pass (bitopic) transmembrane (TM) proteins are highly abundant and functionally diverse, yet their structures remain poorly characterized. Experimental structures of full-length bitopic proteins and their functional complexes are scarce. Although thousands of predicted protein structures from hundreds of proteomes are available, accurately identifying bitopic proteins and determining their orientations within a lipid bilayer remain challenging. Here we developed AlphaPPM, a new pipeline and web server that identifies bitopic proteins from AlphaFold (AF) models and positions them in membranes. Built on the established Positioning of Proteins in Membranes (PPM) and Folding of Membrane-Associated Peptides (FMAP) approaches, AlphaPPM automatically removes low-confidence unstructured regions and signal sequences, separates structural domains, and then reassembles and orients extracellular, intracellular, and TM domains relative to membrane boundaries. We applied and validated the method across complete sets of bitopic proteins from 18 organisms. The results are available through MembranomeX, a dedicated web resource comprising an online database and four web servers. Supplemented by manual curation and cross-validation, the database contains membrane-oriented AF models of 16,697 bitopic proteins organized into 15 functional classes, 664 superfamilies, and 2497 families and data on 7966 protein complexes. Notably, the workflow identified 1225 bitopic proteins absent from established databases. MembranomeX integrates tools for structural modeling, data retrieval, model verification, and 3D visualization, providing a comprehensive platform for evolutionary, structural, and functional analyses of bitopic proteins.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"29 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374740","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":"Surrogate Machine Learning Models for ADME Prediction: From Synthetic Labels to Experimental Data","authors":"Giulia Peteani,Raquel Parrondo-Pizarro,Grégori Gerebtzoff,Raquel Rodríguez-Pérez","doi":"10.1021/acs.jcim.6c02049","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c02049","url":null,"abstract":"Optimization of absorption, distribution, metabolism, and excretion (ADME) properties is central to small-molecule drug discovery, yet is often constrained by limited access to large and consistent experimental data sets. The performance and generalizability of machine learning (ML) models for ADME property prediction rely on the composition of the training data. Surrogate data sets comprise compounds annotated with synthetic ADME labels (predictions) generated by ML models trained on experimental data. This can offer a consistent data augmentation or sharing strategy without requiring access to proprietary data sets; ML models trained on these data can be referred to as surrogate models. However, systematic strategies for developing, evaluating, and applying surrogate models remain underexplored, particularly in the context of transfer learning and validation on new experimental data. In this work, surrogate modeling strategies were investigated for ADME prediction using a data set of over 270,000 compounds with synthetic labels across 25 endpoints. ML models were built on such surrogate data set and evaluated on prospective in-house and public experimental ADME data. Surrogate models based on multitask graph neural networks were predictive under prospective in-house validation and across multiple properties on external data. Uncertainty estimates were informative and actionable, identifying predictions associated with higher or lower prediction errors. Moreover, fine-tuning on experimental data further improved performance for most property data sets. Overall, these results show that surrogate models, especially coupled with fine-tuning, provide a practically valuable approach for ADME prediction, supporting model development from consistently generated data and bringing new opportunities for ML models for property prediction.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"10 2 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374733","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":"FusioBERT: A Task-Adapted Peptide Language Modeling Framework for Anti-Fusarium Peptide Discovery","authors":"Siyu Zhang,Lijuan Mao,Ligang Dong,Chulong Zhang","doi":"10.1021/acs.jcim.6c02852","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c02852","url":null,"abstract":"Fusarium pathogens cause substantial crop losses, motivating targeted antifungal peptide discovery. We developed FusioBERT, a 7.52-million-parameter peptide language model incorporating fixed sinusoidal positional encoding, learnable relative position bias, RMSNorm, and SwiGLU. It was pretrained on 19.18 million short sequences and 12,616 candidates mined from 232 Trichoderma genomes. The curated benchmark comprised 210 labeled peptides; evaluation used five balanced 178-sequence subsets with three-seed 5-fold cross-validation. FusioBERT achieved an MCC of 0.948 ± 0.058 and generated continuously differentiated genome-scale rankings, whereas physicochemical classifiers exhibited score saturation. Across nine external peptide tasks, FusioBERT obtained a mean MCC of 0.685, compared with 0.666 for PepBERT-large and 0.654 for ESM2. Fixed-seed residue-order shuffling reduced MCC from 0.947 to 0.874, providing preliminary evidence of sequence-order sensitivity beyond bulk composition. Among the three evaluated prioritization strategies, FusioBERT-only ranking yielded the most favorable descriptive wet-laboratory outcomes, including the strongest broad-spectrum candidates, SR1403–2 and SR1403–3. Their Hill-logistic EC50 values ranged from 1.62 to 7.07 μM; observed MIC end points were 8–32 μM. Physicochemical fusion, docking, and molecular dynamics were treated as auxiliary analyses. FusioBERT supports model-first prioritization but does not directly establish activity probabilities, candidate safety, or universal homology-independent generalization.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"20 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374735","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":"Expert-Augmented Deep Learning Approach for Synthesis Route Evaluation","authors":"Yujia Guo,Marco V. Mijangos,Tat Hong Duong Le,Samuel Genheden,Varvara Voinarovska,Giulia Bergonzini,Ola Engkvist,Samuel Kaski,Mikhail Kabeshov","doi":"10.1021/acs.jcim.6c03207","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c03207","url":null,"abstract":"Selecting multistep synthetic routes remains a major bottleneck in computer-aided synthesis planning: high-quality negative data are scarce, and individual reported routes provide incomplete benchmarks for route quality. We introduce an expert-augmented framework that combines large-scale reference-based learning with expert-defined chemical feasibility assessment. Large-scale pretraining on literature-derived reference routes achieves 60.3% top 1 reference-route recovery on held-out targets. In expert assessment, the model’s top-ranked route is feasible in 88.4% of assessed cases, either matching a reference route or representing an alternative endorsed by experts. Among alternative top-ranked proposals, 75.4% are judged feasible, demonstrating that divergence from a literature reference does not necessarily indicate chemical infeasibility. The pretrained model captures a measurable expert-feasibility signal (Spearman |ρ| = 0.46), which increases to 0.78 ± 0.05 after expert fine-tuning. On an independent multi-expert dataset, agreement with the expert majority increases from 48.0% to 64.0% after fine-tuning, comparable to the 64.8% agreement between a held-out expert and the majority of their peers. Together, these results show that large-scale proxy supervision can provide scalable route prioritization, while limited expert supervision substantially improves alignment with chemical feasibility.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"23 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374737","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":"Large-Scale and Systematic Prediction of ADMET Properties through Heterogeneous Network and Kernel-Based Deep Learning","authors":"Yong-Cui Wang,Tian-Ze Li,Ji-Jun Chen","doi":"10.1021/acs.jcim.6c01629","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c01629","url":null,"abstract":"Accurately predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties remains a major challenge in accelerating drug discovery, especially for underexplored natural products (NPs). Herein, we present a unified multitask learning framework that integrated heterogeneous network (HN) embeddings and a modified kernel-based deep neural network (DNN) to simultaneously predict all ADMET properties. Our model named as hdADMET captures complex molecular interactions through HNs that incorporated compound–drug relationships, experimental validated ADMET features, and the interrelationships among ADMET properties to transform the embedding representations into a kernel-based DNN for boosting the learning ability of the prediction model. Hold-out cross-validation on benchmark data sets, including ChEMBL, BindingDB, CTRPandGDSC, and NPASS, demonstrated improved and robust accuracy through data integration. Compared with the state-of-the-art multitask models, the hdADMET model achieved competitive performance with a significantly lower computational cost. Its practical utility was further demonstrated by accurately predicting the ADMET profiles of newly identified NPs mined from the literature studies. The promising candidate with high anticancer activity, low toxicity, and favorable metabolic properties was identified for further experimental validation. The hdADMET model offered an alternative way to prioritize leading compounds for clinical translation through HN embeddings and deep learning.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"64 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149374736","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":"Drugging Pockets at the Surface of c-MYC Oncoproteins Revealed by Well-Tempered Metadynamics","authors":"Yanhong Ge,&nbsp;Huixia Lu,&nbsp;Jordi Marti","doi":"10.1021/acs.jcim.6c02197","DOIUrl":"10.1021/acs.jcim.6c02197","url":null,"abstract":"<p>The MYC oncogene encodes a master transcription factoressentialfor regulating cellular homeostasis, growth, and metabolism. However,its deregulation is involved in up to 70% of human cancers, whereit drives uncontrolled cellular proliferation. Structurally, MYC ischaracterized by an N-terminal transactivation domain and a C-terminalbHLHLZ domain critical for DNA binding and dimerization with MAX.Despite its clinical significance, MYC has remained historically “undruggable”due to its intrinsically disordered structure and the absence of traditionalbinding pockets. In this study, we combined all-atom Molecular Dynamicsand well-tempered metadynamics simulations to map the conformationallandscape of a MYC motif in aqueous ionic solution. The selected energy-minimizedstructures exhibit stable conformations and drugging pockets, providingstructural foundations for targetting c-MYC. Furthermore, both theelectrostatic potential and docking results for the obtained structureand the MYC inhibitor’s 10074-G5 demonstrated excellent fit,highlighting its potential as a drug target. This inhibitor, introducedin several previously unpublished drugging pockets, maintained a steadybinding to MYC over unbiased trajectories lasting 1 μs. Thesefindings provide a previously uncharacterized structural foundationfor the design of innovative therapeutic interventions, such as small-moleculeinhibitors or peptides aimed at disrupting oncogenic MYC activity.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 21","pages":"14025–14037"},"PeriodicalIF":6.4,"publicationDate":"2026-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229972","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":"Geometry-Aware Molecular Representation Learning for Approximating NORMAN-Predicted Retention Time Indices in Nontargeted Screening","authors":"Hui Wang,&nbsp;Shifa Zhong,&nbsp;Bin Dong,&nbsp;Zuxin Xu","doi":"10.1021/acs.jcim.6c02699","DOIUrl":"10.1021/acs.jcim.6c02699","url":null,"abstract":"<p>Retention information provides chromatographic evidencecomplementaryto mass spectrometric data in LC-HRMS nontargeted screening (NTS),yet many existing models rely mainly on 1D SMILES or 2D moleculargraphs and make limited use of three-dimensional structure. Here,we developed ViSRTI (Vector-Interaction-Spatial Retention Time Index),a ViSNet-based geometry-aware framework that integrates 3D molecularrepresentations with multimodal descriptors to model the precomputedpositive- and negative-mode RTI labels in NORMAN SusDat. On fixedheld-out test sets, ViSRTI-G outperformed all three baselines. Relativeto the best-performing baseline for each metric, R² increasedby 0.022 (vs GNN-RT) and 0.025 (vs SMILES-BiLSTM), while MAE decreasedby 5.39 and 6.56 RTI units (both vs SMILES-BiLSTM) in positive andnegative modes, respectively. Incremental feature integration andSHAP-guided retraining identified ViSRTI-GP with K_OC andLogP masked for positive mode and ViSRTI-GPT for negative mode, achievingR² values of 0.9433 and 0.9258 and MAEs of 40.42 and 40.76RTI units, respectively. Under Bemis–Murcko scaffold-disjoint5-fold cross-validation, R² remained at 0.9114 ± 0.0126and 0.8937 ± 0.0117, indicating substantial agreement for unseenscaffold groups. Laboratory-specific RT-RTI calibration using 21 standardsyielded R² values of 0.8575 and 0.9525 in positive andnegative modes. In a benzo[a]pyrene/benzo[e]pyrene case study, calibration-derivedexpected RTs differed from measured values by only 0.28 and 0.11 min,respectively, and reproduced the observed elution order. Overall,ViSRTI provides a geometry-aware surrogate for NORMAN-predicted RTIand enables laboratory-specific conversion of predicted RTI into complementaryretention-time constraints for candidate evaluation in NTS.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"66 21","pages":"13902–13912"},"PeriodicalIF":6.4,"publicationDate":"2026-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149250817","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":"Membrane Lipid Composition and Asymmetry Modulate Translocation Versus Leakage of the Model Amphipathic Peptide MAP","authors":"Eric Catalina-Hernandez,Mateo Calle-Velasquez,Mohsen Habibnia,Marcel Aguilella-Arzo,Ramon Barnadas-Rodríguez,Victor A. Lorenz-Fonfria,Alex Peralvarez-Marin","doi":"10.1021/acs.jcim.6c01742","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c01742","url":null,"abstract":"Cell-penetrating peptides (CPPs) can enable intracellular access while avoiding cytotoxicity, yet their behavior is highly sensitive to membrane composition. Here we show that lipid composition and leaflet asymmetry act as a switch that determines whether the amphipathic CPP MAP undergoes nondisruptive translocation or stabilizes membrane pores that drive leakage. Using high-field computational electrophysiology (CompEL) simulations in membranes of increasing physiological relevance, we show that electrically induced membrane defects allow the insertion of MAP peptides. We find symmetric anionic bilayers favor MAP insertion coupled to transmembrane pore stabilization in POPC/POPG membranes, reproducing progressive dye release in liposome leakage assays. Incorporation of cholesterol reduces the number of inserted peptides and conducts to faster leakage kinetics in cholesterol-containing vesicles. In contrast, a minimal asymmetric membrane model (POPC extracellular leaflet; POPC/POPS intracellular leaflet) supports MAP translocation without sustained membrane disruption. This delivery-relevant mechanism is supported by efficient MAP internalization in HEK293 cells while maintaining high viability. Polarized ATR–FTIR provides structural context, indicating predominantly membrane-associated α-helical conformations across lipid compositions. Together, these results establish lipid asymmetry and composition as actionable biointerface parameters that tune CPP function between translocation and leakage and demonstrate an experimentally benchmarked framework for predicting membrane outcomes across complex lipid environments.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"78 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149368314","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":"BFEE3: An AI-Assisted, Feature-Rich, and Streamlined Platform for Absolute Binding Free Energy Calculations","authors":"Mengchen Zhou,Hengwei Bian,Xueguang Shao,Wensheng Cai,Haohao Fu","doi":"10.1021/acs.jcim.6c02494","DOIUrl":"https://doi.org/10.1021/acs.jcim.6c02494","url":null,"abstract":"Absolute binding free energy (ABFE) calculations for protein–ligand and protein–protein complexes, based on molecular simulations, are now established as essential tools in drug design and protein engineering. Nevertheless, both geometrical and alchemical routes require multistage simulation campaigns involving input preparation, postprocessing, and bookkeeping. These complex procedures can introduce preventable errors and limit accessibility for nonexpert practitioners. Therefore, there is a clear need for user-friendly and automated software environments to address these challenges. The Binding Free Energy Estimator (BFEE) is one such tool that has been widely adopted for estimating binding free energies. In this work, we present BFEE3, a release that delivers two major advances. First, it integrates an artificial intelligence (AI) assistant that enables the configuration of binding free energy protocols through natural language and provides on-demand explanations of methodological differences and the roles of individual options. Second, it incorporates recently developed protein–ligand ABFE protocols based on the double-decoupling method (DDM), including lucid DDM (LDDM) and well-tempered metadynamics-λ adaptive biasing force-accelerated DDM (WTM-λABF-DDM). Additional enhancements include support for protein–protein ABFE calculations, GROMACS compatibility for geometrical protein–ligand workflows, one-click simulation setup, and other usability improvements. We demonstrate the main advances of BFEE3 on three representative protein–ligand systems, focusing on natural-language-driven setup, automated input generation, postprocessing, and convergence analysis. We anticipate the use of BFEE3 by both expert and nonexpert users across physical chemistry, biochemistry, and medicinal chemistry.","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":"14 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149368320","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}