Molecular Diversity最新文献

{"title":"Machine learning-driven drug repurposing and computational validation for Nipah virus.","authors":"Shivangi Sharma, Pragya D Yadav, Sarah Cherian","doi":"10.1007/s11030-026-11708-x","DOIUrl":"https://doi.org/10.1007/s11030-026-11708-x","url":null,"abstract":"<p><p>The recurring Nipah virus outbreaks and the lack of effective antiviral therapies, emphasize the urgent need for effective therapeutic interventions. Given the sporadic and unpredictable nature of NiV outbreaks, drug repurposing offers a time-efficient alternative to the development of novel antivirals. In this study, we leveraged machine learning (ML) techniques to accelerate the process of identifying potential therapeutic candidates. Several supervised ML models such as Support Vector Machines, Random Forest, Logistic Regression, Decision Tree, k-Nearest Neighbors, Artificial Neural Networks, and Ridge Classifier were implemented using publicly available NiV inhibitor datasets (viz. Anti-Nipah, NVIK, PubChem) as well as literature review (n = 211 compounds). Among these, the Random Forest model demonstrated highest predictive performance, achieving high accuracy on both training (95%) and testing (86%) datasets. The optimized model was subsequently applied to screen FDA-approved, preclinical, clinical, and antiviral drug libraries (comprising 9021 compounds) to identify potential anti-NiV candidates. The shortlisted compounds underwent further validation through molecular docking to evaluate binding affinity and molecular dynamics simulations to assess structural stability and interactions with key viral targets, including the glycoprotein and RNA-dependent RNA polymerase (RdRp). Based on docking scores and molecular dynamics stability, we identified three and five promising candidates: 2,3,4,5,6-Pentagalloylglucose, Echinacoside, Parishin A, and Neohesperidin dihydrochalcone, Naringin dihydrochalcone, Diosmin, Orientin, Amikacin, against the glycoprotein and RdRp respectively. This integrative approach, combining ML, drug repurposing, and computational validation, aims to expedite the discovery of effective therapeutic agents against the Nipah virus and strengthen preparedness for future outbreaks.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849409","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":"Integrated multi-omics, machine learning, network toxicology, and molecular docking reveal potential mechanisms underlying methyl 4-hydroxybenzoate-associated breast cancer.","authors":"Chunhong Li, Xin Zeng, Yuhua Mao","doi":"10.1007/s11030-026-11712-1","DOIUrl":"https://doi.org/10.1007/s11030-026-11712-1","url":null,"abstract":"<p><p>Breast cancer (BC) represents a major public-health burden, and epidemiological evidence suggests a potential association with exposure to methyl 4-hydroxybenzoate (MEP), a widely-used cosmetic preservative and estrogen-mimicking endocrine-disrupting chemical. Nevertheless, the potential mechanisms underlying MEP-associated BC oncogenesis and progression remain poorly understood. BC-related targets were curated from CTD, GeneCards, and OMIM, whereas MEP-related targets were interrogated from ChEMBL, PharmMapper, and SEA using stringent filters. The intersecting targets informed subsequent protein-protein interaction network construction and molecular docking studies. Subsequently, consensus molecular subtypes of BC were derived by applying ten clustering algorithms to multi-omics data, which were subsequently employed in three machine learning algorithms to develop a consensus MEP-related signature (CMEPRS) for BC patients. Five core putative toxicological targets (HSP90AA1, CTNNB1, TP53, MYC, and EGFR) with critical regulatory roles in MEP-associated molecular alterations were identified. Based on these findings, we generated MEP-toxicity-related classifiers and the CMEPRS prognostic model, which may facilitate patient stratification and support personalized clinical management for BC patients. The high-CMEPRS patients displayed prominent infiltration of macrophages, myeloid-derived suppressor cells, and cancer-associated fibroblasts. Apart from lapatinib, the high-CMEPRS patients showed higher predicted sensitivity to most conventional chemotherapeutic drugs. This computational study provides preliminary insights into molecular alterations linked to MEP exposure and offers a feasible analytical framework for patient stratification and therapeutic-target exploration in breast cancer.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849464","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":"Interpretable QSAR modelling for PPAR-γ agonist prediction by integrating a stacking strategy, docking, and MD simulations.","authors":"Phasit Charoenkwan, Ittipat Meewan, Nalini Schaduangrat, Watshara Shoombuatong","doi":"10.1007/s11030-026-11700-5","DOIUrl":"https://doi.org/10.1007/s11030-026-11700-5","url":null,"abstract":"<p><p>Peroxisome proliferator-activated receptor gamma (PPAR-γ) is a ligand-activated nuclear receptor involved in adipogenesis, glucose homeostasis, lipid metabolism, and inflammation, making it an important therapeutic target for metabolic disorders. However, the complex pharmacology of PPAR-γ presents significant challenges for rational drug discovery. In this study, we developed Meta-iPPAR, an integrative in silico framework combining stacked machine learning, molecular docking, and molecular dynamics (MD) simulations for the identification of PPAR-γ agonists. Meta-iPPAR was constructed using diverse SMILES-based molecular descriptors and multiple machine learning algorithms integrated through a stacking strategy. The proposed model achieved strong predictive performance on the independent test set, with an ACC of 0.926, AUC of 0.965, and MCC of 0.848. Scaffold analysis and SHAP interpretation further identified important chemotypes and molecular features associated with PPAR-γ activation. Large-scale virtual screening of more than 36,000 compounds from the natural product atlas identified three promising fungal-derived candidates. Subsequent docking and 300 ns MD simulations demonstrated stable binding conformations and favorable interactions with key residues in the PPAR-γ ligand-binding domain, comparable to known agonists and co-crystal ligands. Collectively, these findings suggest that Meta-iPPAR provides a reliable computational framework for screening and prioritizing potential PPAR-γ agonists in early-stage drug discovery. Future experimental validation and biological evaluation of the identified compounds are warranted. We anticipate that Meta-iPPAR will be an effective computational tool for screening and prioritizing potential compounds targeting PPAR-γ in the early stage of drug development pipelines.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849394","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":"Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations.","authors":"Rujia Zhao, Tianzhu Shen, Tongzhou Huang","doi":"10.1007/s11030-026-11705-0","DOIUrl":"https://doi.org/10.1007/s11030-026-11705-0","url":null,"abstract":"<p><p>The serotonin transporter (SERT) plays a pivotal role in inflammatory responses and is a central therapeutic target for depression. Consequently, identifying potent SERT inhibitors remains a high priority in early-stage drug discovery. In this study, we evaluated twelve regression models, among which LightGBM, Random Forest, and XGBoost exhibited superior predictive performance, yielding R² values of 0.7138, 0.7001, and 0.6920, respectively. Leveraging these optimized machine learning models, we conducted a large-scale virtual screening of over 11.5 million compounds, identifying 24 promising candidates. Subsequent molecular dynamics (MD) simulations and MM/GBSA binding free energy calculations supported the structural stability and strong predicted binding affinities of three lead molecules: Z2215663922, 19,835,875, and Z310319934. Furthermore, ADMET profiling indicated generally acceptable pharmacokinetic properties, although potential hERG-related liabilities for certain candidates warrant further experimental scrutiny. Our findings provide structurally diverse scaffolds and a computational prioritization framework for early-stage discovery and optimization of SERT inhibitor candidates that merit subsequent experimental validation.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849434","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":"Natural products mediate ferroptosis and immune microenvironment-linked sensitization in osteosarcoma: from chemotherapy resistance to combined therapeutic transformation.","authors":"Yuwen Dai, Wanjun Ding","doi":"10.1007/s11030-026-11707-y","DOIUrl":"10.1007/s11030-026-11707-y","url":null,"abstract":"<p><p>Osteosarcoma is a primary bone tumor in adolescents and young adults, characterized by high chemotherapy resistance and poor prognosis. Ferroptosis, an iron‑dependent and lipid-peroxidation‑driven cell death, has become a key target to overcome chemoresistance and inhibit tumor progression. Natural products, with structural diversity, multi‑target regulation, and low toxicity, show great potential in ferroptosis‑based osteosarcoma therapy. This review summarizes the core molecular mechanisms of ferroptosis, focusing on the regulatory networks of Xc⁻-GSH-GPX4, Nrf2/HMOX1, p53, MAPK, and STAT3 pathways in osteosarcoma. It further categorizes natural products (flavonoids, terpenoids, alkaloids, naphthoquinones, and isothiocyanates) and discusses their targets and mechanisms in inducing ferroptosis. Current bottlenecks, including insufficient mechanistic validation, poor target specificity, limited clinical translation, and a lack of combination therapy strategies, are critically assessed. Future research directions are also proposed. This review aims to provide a theoretical basis and new insights for developing natural-product-based ferroptosis‑targeting drugs to address clinical treatment dilemmas in osteosarcoma.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849382","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":"Computational discovery of HIF-1α/VHL protein-protein interaction inhibitors for hypoxic cell protection.","authors":"Binglin Huang, Bijuan Lin, Xiao Chen, Yifan Zhu, Na Li, Bin Zheng, Weiwei Zheng, Xin Xue, Maobai Liu","doi":"10.1007/s11030-026-11706-z","DOIUrl":"https://doi.org/10.1007/s11030-026-11706-z","url":null,"abstract":"<p><p>The HIF-1α/VHL protein-protein interaction regulates cellular hypoxic adaptation. Targeting this PPI offers therapeutic potential for ischemia, yet the structural diversity and direct cytoprotective applications of reported VHL ligands remain limited. Here, we developed a computational virtual-screening workflow informed by deep learning-based interface analysis. DDMut-PPI was used to nominate putative auxiliary interface residues for construction of an alternative pharmacophore model. Screening of 24,893 molecules followed by fluorescence-polarization testing identified four candidates with IC₅₀ values below 10 μM. Cmpd16 (CAS 1072833-77-2; ixazomib) showed the highest measured affinity in this panel (IC₅₀ = 0.41 μM). Cmpd16 showed no detectable loss of viability under the reported assay conditions and produced a VHL-dependent pattern of HIF-1α and hydroxylated HIF-1α stabilization. In an oxygen-glucose deprivation/reoxygenation model, 10 μM Cmpd16 improved endothelial-cell migration and tube formation and was associated with increased VEGF and GLUT1, reduced ROS accumulation, and reduced cleaved caspase-3. Three independently initialized 200-ns Desmond production simulations showed recurring Pro99 and His110 contacts but replica-dependent protein and ligand dynamics, supporting a computationally plausible rather than unique Cmpd16 orientation. The comparison between the two selected ten-compound panels remained exploratory (two-sided Fisher's exact test, p = 0.0867), and residue causality requires experimental mutagenesis.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148849411","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":"Design, synthesis and biological evaluation of quinazoline-carbazole derivatives as dual TXNIP/DYRK1A inhibitors for the treatment of type 2 diabetes mellitus.","authors":"Li Guan, Wanzhen Su, Zhu Mi, Jing Wang, Yanan Li, Pengfei Song, Wenxia Han, Tongxuan Bai, Wenling Fei, Kejing Lao, Xiaocheng Li, Aiyun Li, Weize Li","doi":"10.1007/s11030-026-11690-4","DOIUrl":"https://doi.org/10.1007/s11030-026-11690-4","url":null,"abstract":"<p><p>TXNIP and DYRK1A are two novel drug targets for the treatment of type 2 diabetes (T2DM), and their respective inhibitors have been shown to suppress islet β-cell apoptosis and promote islet β-cell proliferation. The quinazoline scaffold constitutes the core pharmacophore of TXNIP inhibitors. Harmine, a prototypical DYRK1A inhibitor bearing a β-carboline scaffold, has demonstrated robust β-cell proliferative activity. Carbazole serves as a structurally simplified bioisostere of β-carboline. Guided by the principles of multi-target drug design and combinatorial chemistry, we designed and synthesized a series of quinazoline-carbazole hybrids as dual TXNIP/DYRK1A inhibitors. Compounds PF-5 and PF-6 markedly attenuated palmitic acid (PA)-induced β‑cell injury by suppressing the TXNIP-NLRP3-IL-1β signaling axis. Meanwhile, compounds PF-6 and PF-8 enhanced β-cell proliferation via DYRK1A inhibition. Molecular docking confirmed PF-6 bound with high affinity to both targets, and ADMET predictions supported its drug-like properties. Thus, PF-6 emerges as a potential dual-target therapeutics for T2DM. In conclusion, PF-6 can be used as a potential new chemical entity against T2DM.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148786808","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":"Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.","authors":"Jingwen Wang, Shuan Liu, Shaoye Zhang, Jianbo Qiao, Guanhe Li, Hanjun Zhao, Xiangxiang Zeng, Jinjin Li, Changhang Lin, Chen Su, Leyi Wei","doi":"10.1007/s11030-026-11694-0","DOIUrl":"https://doi.org/10.1007/s11030-026-11694-0","url":null,"abstract":"<p><p>Retrosynthetic planning is a core task in computer-aided synthesis design. Existing multi-step retrosynthesis methods mainly focus on route accessibility and search success rates. However, in practical synthesis, route quality depends partly on yield, whose importance may vary across applications. Existing methods often struggle to adjust their search strategies according to changing yield preferences. To address this problem, we propose Hyp-Retro, a hypernetwork-guided and preference-conditioned retrosynthetic planning model. Hyp-Retro first collects candidate-pool decision data through a yield-guided search process. It then constructs a hypernetwork-driven tree policy network conditioned on yield preference, allowing the model to adjust candidate-node scores under different preference settings. On this basis, online reinforcement learning is employed to fine-tune the policy network, further enhancing the model's long-horizon decision-making capability and preference alignment in realistic search environments. Experimental results show that Hyp-Retro outperforms comparative methods in terms of search success rate and route yield. Moreover, it can adaptively adjust its planning strategy under different yield preferences, thereby generating high-quality retrosynthetic routes that better satisfy target-specific requirements.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148786760","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":"Molecular active learning approaches for predicting skin cytotoxicity.","authors":"Sastiya Kampaengsri, Darlene Nabila Zetta, Andi Endang Kusuma Intan, Huynh Anh Duy, Tarapong Srisongkram","doi":"10.1007/s11030-026-11689-x","DOIUrl":"https://doi.org/10.1007/s11030-026-11689-x","url":null,"abstract":"<p><p>Skin cytotoxicity assessment of small molecules is vital for assessing their risk of cytotoxicity upon topical contact. However, identifying cytotoxic compounds within vast chemical spaces is costly and time-consuming. In this study, we developed a data-efficient training approach to reduce the cost and time of cytotoxicity screening using an active deep learning framework. To mimic a real-world drug screening scenario, an experimental budget was imposed, allowing only a limited number of molecules to be selected from a large, unlabeled pool for cytotoxicity evaluation. A curated cytotoxicity dataset against keratinocytes was constructed. The active learning (AL) experiments were conducted using three deep learning (DL) models with seven different acquisition function strategies. From these experimental setups, we found that the self-supervised autoencoder acquired cytotoxic enrichments better than random chance and the other two baseline models. Secondly, we found that the top-k highest-probability acquisition function identified more new cytotoxic molecules than the other acquisition strategies. However, when measuring the diversity of acquired cytotoxic and non-cytotoxic molecules, we found that the novelty and diversity demonstrated the highest structural differences compared to other acquisition functions. Thirdly, we found that the overall cytotoxic enrichments from the best AL model (77.0 ± 4.0) are greater than those from baseline one-shot learning (71.3 ± 4.7), indicating that this AL method is useful for identifying more cytotoxic molecules. Next, we found that the AL model attains performance on par with a one-shot training approach that uses 100% of the pool data, while the AL model trained on only 16.6% of the pool data demonstrates a low data requirement for model training. Furthermore, we validated the predictive performance of the new AL model on cytotoxicity testing, exhibiting that the method is effective for prioritizing cytotoxic molecules in low-resource settings for drug design and development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148786749","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":"SCAD-DTA: spherical concept alignment with dynamic multi-modal fusion for drug-target affinity prediction.","authors":"Zhensong Wang, Weiliang Han, Ge Kong, Yuan Zhang, Jiajie Xing, Juan Wang","doi":"10.1007/s11030-026-11677-1","DOIUrl":"https://doi.org/10.1007/s11030-026-11677-1","url":null,"abstract":"<p><p>Accurate drug-target affinity (DTA) prediction is essential for virtual screening and computational drug discovery. However, existing benchmarks such as Davis and KIBA often suffer from imbalanced label distributions and potential sampling bias, which can lead to overestimated performance and limited generalization in realistic settings. In addition, integrating heterogeneous modalities such as molecular graphs, protein sequences, and chemical fingerprints remains challenging due to unstable cross-modal interactions and inconsistent representation scales. To address these issues, we first construct three curated datasets from the Therapeutic Target Database (TTD), namely TTD_IC50, TTD_EC50, and TTD_KI, using a stratified sampling and normalization strategy to improve distribution balance while maintaining biochemical diversity. These datasets are designed to better reflect real-world distribution shifts in DTA prediction. Based on these datasets, we propose SCAD-DTA, a geometry-aware multimodal learning framework that explicitly addresses instability in cross-modal fusion under distribution shift. The key idea is to stabilize multimodal interaction by jointly modeling adaptive fusion dynamics and representation geometry constraints. SCAD-DTA introduces a Dynamic Cross-Modal Attention mechanism that adaptively reweights modality contributions conditioned on sample-specific context, mitigating modality dominance. To further improve representation stability, a Spherical Constrained Projection module enforces unit-norm geometry in the latent space, reducing scale inconsistency across modalities. In addition, a Concept Alignment module maps fused representations into a learnable prototype space, enabling structured and interpretable modeling of drug-target interactions. Extensive experiments on six benchmark datasets show that SCAD-DTA achieves competitive or superior performance in most evaluation settings, with particular strength under cold-start and cross-distribution scenarios; however, its gains are less pronounced in some cold-start splits (e.g., cold-drug on KIBA, cold-target on Metz), which we discuss explicitly. The source code and datasets are publicly available at: https://github.com/xwtxbzz/SCAD-DTA.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148786756","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}