Molecular Diversity最新文献

{"title":"Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer's disease.","authors":"The-Huan Tran, Thai-Son Tran, Thanh-Dao Tran","doi":"10.1007/s11030-026-11725-w","DOIUrl":"https://doi.org/10.1007/s11030-026-11725-w","url":null,"abstract":"<p><p>Alzheimer's disease is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction and neuroinflammation. Dual inhibition of acetylcholinesterase and monoacylglycerol lipase has emerged as a promising therapeutic approach. This study employed an integrative in silico workflow to identify potential dual acetylcholinesterase/monoacylglycerol lipase inhibitors from a molecular library derived from known inhibitors (rivastigmine, JZL-184, ABX-1431). A total of 365 compounds were screened via molecular docking, interaction-based filtering, ADME/toxicity prediction, and molecular dynamics simulations. Among them, compound H34 demonstrated a comparatively favorable overall computational profile, supported by MM/GBSA binding-energy estimates (ΔG_bind = - 30.96 and - 37.34 kcal/mol) and comparatively favorable structural stability metrics (RMSD, RMSF, Rg, and SASA) in the MD simulations. Further ProLIF interaction mapping and free energy landscape analysis supported the persistent interaction profile and conformational behavior of the H34-protein complexes. Additionally, H34 displayed favorable pharmacokinetic properties and low predicted acute toxicity. These results highlight H34 as a promising dual-target candidate for Alzheimer's disease therapy and illustrate the effectiveness of integrated computational strategies in early-stage drug discovery.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885728","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":"Integrating machine learning and deep learning with multiple molecular fingerprints for topoisomerase I inhibitor screening and lead identification.","authors":"Huang Zeng, Xuerou Zheng, Jiayao Liu, Shengyuan Zhang, Hua Nie, Nan Wang, Lingfeng Wu, Chunfang Liu, Ming Zhai, Hao Yang, Jiunlong Yang, Bo Qiu","doi":"10.1007/s11030-026-11709-w","DOIUrl":"https://doi.org/10.1007/s11030-026-11709-w","url":null,"abstract":"<p><p>Topoisomerase I (TOP1) is a crucial anticancer target, but the development of traditional TOP1 inhibitors suffers from long research cycles, high costs, and low success rates. Existing artificial intelligence (AI)-driven studies lack systematic comparisons of molecular fingerprints and algorithms, as well as user-friendly predictive application tools. To address these gaps, this study retrieved TOP1 inhibitor activity data from the ChEMBL database, integrated five types of molecular fingerprints (AtomPairs, MACCS, Morgan, PharmacoPFP, and RDKitDes), and constructed and compared classical machine learning (ML) models and deep learning (DL) models, resulting in a total of 40 models. The four top-performing models, SVM::Morgan, RF::Morgan, DNN::MACCS, and KNN::Morgan, achieved ROC-AUC values of 0.93-0.94 under random splitting. Y-scrambling supported that the models learned non-random structure-activity relationships, while SHAP analysis identified key molecular features. The URL of the developed web application is http://drugpred.top:5000 , and this application enables the prediction of TOP1 inhibitory activity via SMILES (Simplified Molecular-Input Line-Entry System) or molecular structure drawing. Additionally, standalone desktop applications (.exe) for offline prediction are freely available at https://github.com/zenghuang8006/TOP1-inhibitor-prediction . Screening of 189,554 SPECS compounds followed by in vitro validation identified AG60 and AI61 as potential TOP1 inhibitors hits, with inhibition rates of 64% and 90% at 400 µM, respectively. Overall, this study provides a practical computational framework for TOP1 inhibitor screening and identifies promising candidate compounds. Notably, scaffold-split AUC values decreased to 0.67-0.82, indicating reduced extrapolative performance for compounds containing previously unseen scaffolds.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885807","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":"De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.","authors":"Zhizhi Li, Fang Tian, Shuning Jiang, Shanshan Dong, Feifei Tian","doi":"10.1007/s11030-026-11714-z","DOIUrl":"https://doi.org/10.1007/s11030-026-11714-z","url":null,"abstract":"<p><p>Deep learning has greatly advanced de novo protein design, yet its application to rational short peptide design remains underexplored. Here, we developed SPB-Seeker (Short Peptide Binder Seeker), an integrated pipeline combining deep learning-based generative models with computational chemistry screening to discover dual-target short peptide inhibitors. Using penicillin-binding proteins PBP2b and PBP2x from drug-resistant Streptococcus pneumoniae as targets, AFDesign, RFdiffusion, and BoltzGen were employed to generate an initial library of 1101 candidate sequences. Subsequently, ESM2 was employed to extract sequence embeddings for diversity analysis, which revealed distinct algorithmic biases among the three generative models, and was then used as the feature extractor of a prediction framework for early-stage toxicity screening. Candidates were further prioritized through molecular docking, tiered molecular dynamics simulations, and MM/PB(GB)SA binding free energy calculations. Three peptides, AFD1, BG3, and RFD2, showed high binding stability, with BG3 displaying the strongest dual-target binding, achieving binding free energies of - 52.777 kcal/mol for PBP2b and - 74.071 kcal/mol for PBP2x. Interestingly, quantum chemical calculations using cluster model and the Interaction Region Indicator (IRI) method analyses indicated that BG3 adopts a stable cyclic-like conformation when bound to PBP2x, driven by proline-induced turns, intramolecular hydrogen bonds, and terminal C-H···π interactions. Overall, SPB-Seeker provides an extensible computational framework for targeted short peptide binder discovery and offers a basis for subsequent affinity optimization and stability enhancement.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885734","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":"Free energy perturbation and machine learning-assisted identification of novel molecules for the Mycobacterium tuberculosis KasA protein: a fragment-based drug design approach.","authors":"Md Ataul Islam, Mohammad Ajmal Ali, Rupesh Chikhale, Md Lutful Islam, Mohammad Abul Farah","doi":"10.1007/s11030-026-11726-9","DOIUrl":"https://doi.org/10.1007/s11030-026-11726-9","url":null,"abstract":"<p><p>KasA is an essential enzyme of Mycobacterium tuberculosis (Mtb). It plays a critical role in synthesizing long-chain mycolic acids, the major components of the bacterial cell wall, by regulating the FAS-I and FAS-II fatty acid synthesis pathways. Inhibiting KasA offers a promising strategy for treating tuberculosis (TB). This study used fragment-based drug design (FBDD) to design novel small molecules targeting KasA. Fragments from known KasA inhibitors were generated with the MacFrag tool and then combined with Fragmenstein to create potential hit compounds. A multi-tiered molecular docking approach was used to evaluate their binding affinity and interactions with KasA. Selected candidates underwent pharmacokinetic analysis and molecular dynamics (MD) simulations. Five promising molecules, namely KasA_FB1, KasA_FB2, KasA_FB3, KasA_FB4 and KasA_FB5, were identified. Their molecular docking binding energies were - 7.80, - 8.30, - 9.00, - 7.80, and - 9.00 kcal/mol, respectively, all superior to the reference co-crystal ligand TLM (- 7.20 kcal/mol). MD simulations showed that their dynamic stability was comparable to or better than TLM. MM-GBSA and free energy perturbation (FEP) analyses further confirmed their superior binding affinity for KasA. These compounds represent promising candidates for the development of new anti-TB drugs targeting KasA and warrant experimental validation.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885769","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":"FlavorGPN: a graph neural network for multi-label molecular flavor prediction.","authors":"Jie Liu, Xin Shu, Shougang Ren, Sheng Wan, Xingying Pan","doi":"10.1007/s11030-026-11702-3","DOIUrl":"https://doi.org/10.1007/s11030-026-11702-3","url":null,"abstract":"<p><p>Predicting chemosensory attributes from chemical structures is a fundamental task in cheminformatics and molecular modeling. When applied to flavor specifically, this task becomes particularly challenging due to the structural diversity of flavor molecules and the complex, multi-label nature of human sensory perception. Traditional machine learning methods often rely on one-dimensional fingerprints, which inadequately capture high-dimensional topological and geometric features. In this study, we introduce the FlavorGraph Predictive Network (FlavorGPN), a novel graph neural network (GNN) framework for multi-label flavor prediction. FlavorGPN leverages the pretrained 2D graph encoder from GraphMVP, whose parameters are learned through 3D-informed pretraining, to enhance molecular graph representations. Notably, no explicit 3D conformers or atomic coordinates are used during downstream fine-tuning or inference. Therefore, the use of 3D information in this study should be understood as 3D-supervised pretraining rather than direct 2D/3D geometric integration during inference. To mitigate the class imbalance inherent in flavor datasets, we propose ML-ROS-improved, an adaptive oversampling algorithm that integrates dynamic thresholding for minority-label identification, weighted minority-label sampling, and constrained graph augmentation. We also systematically evaluate several graph augmentation strategies. Among them, Molecular Connectivity Index (MCI)-constrained augmentation achieves the highest observed Macro-F1 and Macro AUC-ROC scores. Across the FlavorMiner and FART benchmarks, FlavorGPN achieved the highest observed Macro-F1 and Macro AUC-ROC point estimates among the evaluated baselines under the reported experimental settings. On the FART benchmark, the model achieved a Macro-F1 score of 0.8542 and a Macro AUC-ROC score of 0.9796. Literature-based contextual comparisons further indicate that the unified model performs competitively on key flavor categories, including Sweet, Bitter, and Sour. However, these comparisons do not constitute controlled head-to-head evaluations. Overall, the benchmark results demonstrate the practical value of FlavorGPN for imbalanced multi-label chemosensory prediction under the evaluated settings and suggest its potential to support computational screening and molecular-level analyses of flavor-associated chemical properties.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148885757","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":"New technologies in drug discovery.","authors":"Taoda Shi","doi":"10.1007/s11030-026-11711-2","DOIUrl":"https://doi.org/10.1007/s11030-026-11711-2","url":null,"abstract":"","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148872444","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":"DSC-bsite: a dynamic-static collaborative multimodal graph learning method for protein-small molecule binding site prediction.","authors":"Minglei Dong, Dongjiang Niu, Yuanxing Peng, Hongle Li, Minghao Li, Zhiqiang Wei, Zhen Li","doi":"10.1007/s11030-026-11722-z","DOIUrl":"https://doi.org/10.1007/s11030-026-11722-z","url":null,"abstract":"<p><p>Accurate identification of protein-small molecule binding sites is a fundamental problem in computational biology and drug discovery. Existing sequence-based methods lack explicit spatial awareness, while structure-based approaches often struggle to integrate long-range functional dependencies and semantic information, leading to limited generalization on low-similarity or sparsely annotated proteins. To address these challenges, we propose DSC-BSite, a dynamic-static collaborative multimodal graph learning framework for residue-level binding site prediction. First, a Static Global Sequence Encoding module captures multi-scale local patterns and long-range contextual dependencies from protein sequences. Second, a Gated Dual-Graph Dynamic Propagation (GDDP) module jointly models spatial geometric interactions and sequence-derived functional correlations using a dynamic spatial graph and an attention-guided sequence graph, enabling adaptive residue interaction modeling. Third, a PPI-guided Structural-Semantic Alignment (PSSA) pre-training strategy aligns structural representations with function-aware semantic embeddings, enhancing the biological expressiveness of structural features without requiring PPI information during inference. Experimental results on the UniProtSMB and SJC benchmark datasets demonstrate that DSC-BSite achieves competitive performance across multiple evaluation metrics, with particularly strong results in Recall on UniProtSMB and Precision and MCC on SJC.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860363","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":"Multi-omics, machine learning, and molecular simulation identify CD44 as a candidate target in endocrine-disrupting chemical-associated thyroid cancer progression.","authors":"Yifan Hu, Keshu Liu, Ting Chen, Zhizhen He, Shuang Li, Wu Hu, Qiuyang Fu, Xiong Chen","doi":"10.1007/s11030-026-11721-0","DOIUrl":"https://doi.org/10.1007/s11030-026-11721-0","url":null,"abstract":"<p><p>Endocrine-disrupting chemicals (EDCs) are widely present in the environment and consumer products and may disturb thyroid hormone homeostasis. However, the molecular mechanisms linking EDC exposure to thyroid cancer progression remain unclear. This study integrated toxicity prediction, toxicogenomics, transcriptomic analysis, machine learning, molecular simulation, and experimental validation to identify EDC-related key targets in thyroid cancer. ADMETlab 3.0 was used to evaluate the potential toxicity of BPA, PFOA, DDT, BDE-209, TCDD, and DEHP, and compound-related genes were obtained from the CTD database. By integrating thyroid cancer-related genes from multiple disease databases, 1113 shared EDC-thyroid cancer targets were identified and were mainly enriched in PI3K-Akt, FoxO, and AGE-RAGE signaling pathways. Combined with differential expression analysis, machine learning identified a six-gene diagnostic model consisting of FN1, BCL2, CD44, CDKN1A, CTNNB1, and JUN. The Lasso + LDA model achieved an average AUC of 0.976 across the training cohort and three external validation cohorts. CD44 showed robust diagnostic performance, with AUC values of 0.950, 0.801, 0.878, and 0.938 in the training set, GSE27155, GSE29265, and GSE153659, respectively, and had the highest contribution in SHAP analysis. Immune infiltration, TCGA survival, and single-cell analyses indicated that CD44 was associated with the tumor immune microenvironment, cellular state changes, and prognosis. Molecular docking and 200 ns molecular dynamics simulations generated plausible docking poses of BPA, DEHP, and PFOA on CD44, with PFOA showing the most favorable predicted docking score. Experimental validation showed higher CD44 expression in thyroid cancer tissues and cells and increased CD44 expression following EDC exposure. CD44 knockdown attenuated EDC-associated increases in proliferation, colony formation, and migration. These findings identify CD44 as a candidate molecule associated with EDC-responsive malignant phenotypes in thyroid cancer.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860516","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":"Discovery of aloe-emodin as an antidepressant agent by targeting RANBP9.","authors":"Yangyang Yang, Dezhong Sun, Qingyun Jia, Bing Zhu, Huaiqing Lv, Fengyuan Che","doi":"10.1007/s11030-026-11688-y","DOIUrl":"https://doi.org/10.1007/s11030-026-11688-y","url":null,"abstract":"<p><p>Excitotoxicity is a core pathological mechanism underlying various neurological disorders, including depression. It is primarily driven by the overactivation of NMDA receptors and disruption of calcium homeostasis. However, effective therapeutic interventions targeting this process remain limited. In this study, aloe-emodin was shown to reverse NMDA-induced reduction in cell viability, apoptosis, and calcium overload in a dose-dependent manner, while also attenuating NMDA-induced autophosphorylation of CaMKII. In a chronic unpredictable mild stress (CUMS) model, aloe-emodin significantly ameliorated depression-like behaviors, suppressed inflammatory responses and oxidative stress, and downregulated the expression of cleaved-PARP. By conducting biotin pull-down coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, cellular thermal shift assay (CETSA), drug affinity responsive target stability (DARTS) analysis, and surface plasmon resonance (SPR) analysis, RANBP9 was identified as a direct molecular target of aloe-emodin, with a binding affinity (KD) of 716 nM. Molecular docking and molecular dynamics simulations revealed that aloe-emodin selectively binds to the His332 residue of RANBP9. Aloe-emodin promoted the degradation of RANBP9 via the ubiquitin-proteasome pathway by facilitating the interaction between RANBP9 and the E3 ligase CHIP. After RANBP9 was knocked down, the protective effects of aloe-emodin on cell viability and apoptosis were significantly weakened, confirming that the anti-excitotoxic activity of aloe-emodin is RANBP9-dependent. These findings collectively demonstrate that aloe-emodin is a novel and potent RANBP9-targeting compound with antidepressant activity and suggest that RANBP9 may serve as a promising target for developing antidepressant drugs.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856886","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":"Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors.","authors":"Yuxi Wang, Linxia Fang, Quanfang Liu, Zhiwei Zhang, Cong Xu, Yihui Jiang","doi":"10.1007/s11030-026-11716-x","DOIUrl":"https://doi.org/10.1007/s11030-026-11716-x","url":null,"abstract":"<p><p>Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the G1-to-S phase transition, and their dysregulation is a hallmark of numerous malignancies. Despite the clinical success of existing CDK4/6 inhibitors, there remains a persistent need for chemically diverse scaffolds with potent dual-target affinity. In this study, we developed and implemented a virtual screening workflow that synergistically integrates ligand-based machine learning with structure-based molecular docking. By benchmarking multiple ML algorithms against curated ChEMBL datasets (265 CDK4 inhibitors and 402 CDK6 inhibitors), a Bayesian Ridge regressor utilizing ECFP4 fingerprints was identified as the most predictive model, achieving cross-validated R² values of 0.731 ± 0.022 for CDK4 and 0.721 ± 0.070 for CDK6. This optimized ML filter was deployed to prioritize a 22,823-compound library, followed by rigorous dual-target docking refinement. This strategy prioritized three candidate hits for biochemical evaluation, among which HY-18,623 showed potent dual inhibitory activity, with IC₅₀ values of 3.5 nM against CDK4 and 17.4 nM against CDK6. Extensive 200-ns molecular dynamics simulations and binding free energy analyses elucidated that HY-18,623 achieves high-affinity binding through persistent hydrogen bonds with hinge residues Val96 (CDK4) and Val101 (CDK6). These findings demonstrate that our integrated computational funnel is a highly efficient tool for discovering potent kinase inhibitors and position HY-18,623 as a promising lead candidate for further therapeutic development in oncology.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148856879","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}