Molecular Diversity最新文献

{"title":"Computational discovery of DENV-3 RdRp allosteric inhibitors: biological evaluation and mechanistic studies.","authors":"Gokul Krishnan Nagendran, Swati Fageria, Divya Bhatt, Neetu Malya, Akansha Belwal, Shruti Gautam, Muskan Singhal, Ayesha Fatima, Mamta Choudhary, Vineeta Rai, Chetan D Meshram, Sathish Kumar Mudedla","doi":"10.1007/s11030-026-11647-7","DOIUrl":"https://doi.org/10.1007/s11030-026-11647-7","url":null,"abstract":"<p><p>The RNA-dependent RNA polymerase (RdRp) of Dengue virus (DENV) is a key antiviral target due to its essential and conserved role in viral replication. In this study, we have employed a similarity-guided virtual screening approach to the Maybridge compound library by combining molecular docking and molecular dynamics (MD) simulations with binding free energy calculations to identify and prioritize candidate allosteric site inhibitors for DENV-3 RdRp. A picogreen-based RdRp enzymatic assay was developed and validated for the biological evaluation of the prioritized compounds obtained from computational screening. GK00498 was identified as a moderately active molecule with an IC₅₀ of 23.3 µM in the enzymatic assay. Further, experimental evaluation of GK00498-based molecules generated through the computational workflow reveals that GK00598, GK01138 and SPB05603 are potent inhibitors with IC₅₀ values of 6.78, 7.59 and 7.74 µM, respectively. Mechanistic insights from structural and dynamical analyses of MD simulations suggested an allosteric mode of inhibition that orchestrated changes in RdRp functional motions and disrupted the conformational dynamics required for RNA synthesis. Overall, these findings provide a strong computational and experimental foundation for DENV-3 RdRp inhibitors that can be optimized and evaluated for anti-dengue activity in vitro and in vivo.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434718","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 deep learning and multi-scale modeling for the discovery of pan-genotypic HCV NS5B polymerase inhibitors.","authors":"Di Han, Fengxiang Liu, Xiangbing Peng, Yifan Wang, Zhipeng Shi, Yiwei Xue, Hongkun Yang, Meiting Wang, Jiarui Lu, Taigang Liu, Shaoli Cui, Yongtao Xu","doi":"10.1007/s11030-026-11633-z","DOIUrl":"https://doi.org/10.1007/s11030-026-11633-z","url":null,"abstract":"<p><p>Chronic Hepatitis C Virus (HCV) infection remains a significant global health challenge, necessitating the development of pan-genotypic inhibitors to overcome the limitations of genotype-specific therapies. The NS5B RNA-dependent RNA polymerase, particularly its conserved Palm II region, is a promising target for such broad-spectrum antivirals. In this study, we integrated advanced deep learning and multi-scale computational approaches to design novel pan-genotypic HCV NS5B inhibitors. First, we employed AlphaFold3 to predict the high-resolution structures of NS5B polymerase from genotypes with previously unresolved crystal structures (GT3a, GT3b, GT6a, GT7a, and GT8a), establishing a robust structural database refined by 100 ns molecular dynamics (MD) simulations. Concurrently, the DrugEx algorithm, a multi-objective reinforcement learning-based molecular generator, was used for de novo design, yielding 42,257 novel chemical entities. Subsequent multi-step virtual screening, molecular docking, and 300 ns MD simulations identified four hit compounds (01, 07, 18, 59) that demonstrated promising computational binding profiles and pan-genotypic coverage compared to the positive control BMS-986,139. These compounds exhibited excellent stability in complex with NS5B across ten genotypes, as evidenced by low RMSD fluctuations. MM/GBSA binding free energy calculations and hydrogen bond analysis revealed key interaction mechanisms, including dynamic hydrogen bonding with Tyr448 and strong electrostatic contributions from Arg200, which underpin their broad-spectrum efficacy. Furthermore, ADMET predictions confirmed favorable drug-like properties and synthetic feasibility, supported by retrosynthetic analysis. This work presents a comprehensive \"AlphaFold3-DrugEx-Multi-scale Simulation\" strategy, enabling the efficient discovery of novel, potent, and pan-genotypic HCV NS5B inhibitors candidates with significant potential for further development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417615","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":"Nitrogen atom insertion and related skeletal editing strategies in aromatic heterocycles for drug discovery (2015-2025).","authors":"Jixiang Ni, Mohd Aamir Bin Riyaz, Zhenyu An, Yang Lu, Sajid Muhammad, Chao Zhang, Ning Xi, Yufen Zhao","doi":"10.1007/s11030-026-11658-4","DOIUrl":"https://doi.org/10.1007/s11030-026-11658-4","url":null,"abstract":"<p><p>Nitrogen-containing heterocycles constitute the pharmacophoric core of the majority of clinically approved small-molecule drugs, yet their conventional synthesis relies on scaffold-specific condensation routes that are poorly suited to late-stage structural diversification. The emergence of single-atom skeletal editing, where one nitrogen atom is inserted directly into an intact aromatic ring to transform scaffold identity without rebuilding the molecular periphery, represents a fundamental departure from traditional heterocycle synthesis and provides a powerful new retrosynthetic logic for medicinal chemistry. This review surveys significant advances in nitrogen atom insertion into aromatic N-heterocycles reported between 2015 and 2025, organized by mechanistic platform. We discuss nitrene-mediated insertion using iodonitrene and sulfenylnitrene reagents, transition-metal-catalysed strategies including copper- and cobalt-catalysed ring expansions and carbon-to-nitrogen transmutation of arenols, electrochemical approaches that eliminate stoichiometric oxidants, and emerging photochemical and photolytic methods that enable late-stage modification of complex substrates. For each platform, substrate scope, functional group tolerance, mechanistic underpinning, and practical limitations are critically evaluated. We further highlight how these strategies collectively enable nitrogen scanning in drug discovery, the systematic replacement of carbon with nitrogen within a lead scaffold to modulate potency, selectivity, and pharmacokinetic properties. Key open challenges including enantioselective nitrogen insertion, predictive regioselectivity, and scalable sustainable synthesis are outlined alongside future directions for this rapidly evolving field.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417669","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":"From inhibition to degradation: advances in highly selective targeting strategies for HPK1.","authors":"Longbin Yu, Rui Yan, Yashi Li, Huijie Wu, Zhe-Shan Quan, Xiaoting Li, Qing-Kun Shen","doi":"10.1007/s11030-026-11662-8","DOIUrl":"https://doi.org/10.1007/s11030-026-11662-8","url":null,"abstract":"<p><p>Hematopoietic progenitor kinase 1 (HPK1) is a serine/threonine kinase specific to the hematopoietic system. As a member of the mammalian Ste20-related MAP4K kinase family, HPK1 serves as a key negative regulator of T cell-mediated immune responses. It is implicated in the development of human malignancies. Consequently, HPK1 has emerged as a highly promising target for cancer immunotherapy. Inhibition of HPK1 can abrogate its suppressive effects on T cell activation and function. However, HPK1 shares high structural homology with other MAP4K family members, particularly GLK. This poses a major challenge to the development of highly selective inhibitors. Achieving selectivity by distinguishing HPK1 from other family kinases and key T-cell activation kinases is crucial. It helps minimize off‑target side effects and broaden the therapeutic window. This review summarizes advances in HPK1-targeting strategies from 2016 to 2026. Focusing on the structural classes and selectivity optimization mechanisms of highly selective small-molecule HPK1 inhibitors, as well as breakthroughs in the development of proteolysis-targeting chimeras (PROTACs). The structural biology basis underlying HPK1 selectivity regulation is also analyzed. Finally, this review addresses the challenges in developing HPK1-targeted formulations and discusses future research directions, with the aim of providing a reliable reference for the rational design and clinical translation of novel, highly selective HPK1-targeted therapeutic strategies.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417602","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":"Prediction and mechanistic insights into drug-induced reproductive toxicity through integrated machine learning, FAERS-based signal comparison, and network toxicology analyses.","authors":"Jiawang Yan, Yaofeng Zhou, Yihuan Zhao, Fushan Tang","doi":"10.1007/s11030-026-11654-8","DOIUrl":"https://doi.org/10.1007/s11030-026-11654-8","url":null,"abstract":"<p><p>Drug-induced reproductive toxicity is a critical concern in drug safety evaluation, whereas conventional assessment methods are often constrained by high costs and long experimental cycles. In this study, a machine learning-based predictive model for reproductive toxicity was developed and integrated with data from the FDA Adverse Event Reporting System (FAERS), network toxicology analysis, molecular docking, and molecular dynamics simulation to systematically evaluate the post-marketing reproductive toxicity risk of drugs and explore their potential mechanisms. Among the evaluated machine learning algorithms, LightGBM demonstrated the best overall performance, achieving an F1-score of 0.854, a ROC-AUC of 0.933, a PR-AUC of 0.931, and an MCC of 0.705 on the independent test set, with robust generalization confirmed by ten-fold cross-validation. Among drugs approved between 2015 and 2024, 72 were predicted to have a high risk of reproductive toxicity. FAERS-based signal comparison showed that 55 of these drugs (76.39%) were associated with reproductive toxicity-related adverse event reports, indicating consistency between model predictions and FAERS-reported reproductive toxicity-related adverse events. Network toxicology analysis identified 12 key targets, including ESR1, IGF1, and AKT1, that may be involved in reproductive toxicity. Molecular docking showed that drugs with high predicted reproductive toxicity risk could bind effectively to multiple toxicity-related targets, while molecular dynamics simulations confirmed stable interactions between selected drugs and ESR1, mainly through hydrogen-bonding and hydrophobic interactions. Favorable binding free energies further supported their potential multi-target effects. Overall, this integrated strategy combining predictive modeling with FAERS-based signal comparison provides a useful framework for drug safety evaluation and mechanistic investigation of reproductive toxicity.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417650","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":"Chemical features consistently associated with kinase inhibitor activity: a cross-target SHAP analysis under scaffold-split evaluation.","authors":"Abubakar Siddiq Salihu, Muhammad Sulaiman Rahama, Wan Mohd Nuzul Hakimi Wan Salleh, Nura Suleiman Gwaram, Ahmed Salisu, Sulaiman Sani Yusuf","doi":"10.1007/s11030-026-11664-6","DOIUrl":"https://doi.org/10.1007/s11030-026-11664-6","url":null,"abstract":"<p><p>Interpretable machine learning approaches to quantitative structure-activity relationship (QSAR) modelling are increasingly applied in drug discovery, yet most studies remain confined to single targets and report feature attributions without translating them into chemically meaningful insights. We introduce a cross-target SHAP entropy framework for quantifying shared versus target-specific structure-activity relationships across protein families, applied to 31 human kinase targets from BindingDB under scaffold-based train-test evaluation. Random Forest classifiers trained on Morgan ECFP4 fingerprints achieved a median AUROC of 0.994, AUPRC of 0.9998, and MCC of 0.674, confirming genuine SAR learning beyond class prevalence exploitation. Pairwise Spearman rank correlation of mean absolute SHAP profiles across targets yielded moderate cross-target consistency (mean r = 0.332; 465 pairs). Shannon entropy-based classification of the top 200 fingerprint bits identified 15 consensus features dominated by aromatic N-heterocycles, aliphatic rings, and hydrogen bond environments, and 50 divergent features showing 2.8-fold higher SHAP magnitude than consensus features. SAR validation confirmed genuine enrichment of two top consensus fragments in active compounds. These findings indicate that kinase QSAR models share a low-magnitude consensus descriptor signal across the kinase family, while target-specific features dominate predictive decision boundaries. All SHAP attributions describe model decision behavior and should not be interpreted as causal binding mechanisms. The entropy decomposition framework is generalisable to other protein families and provides a transferable workflow for converting SHAP outputs into chemically actionable insights. All code and data are publicly available.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417548","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":"DeepPepQSAR: all-in-one for comprehensively exploiting the vast molecular diversity space of bioactive peptide universe.","authors":"Peng Zhou, Kexin Li, Yulu Gan, Yunyi Zhang, Li Mei, Shuyong Shang","doi":"10.1007/s11030-026-11665-5","DOIUrl":"https://doi.org/10.1007/s11030-026-11665-5","url":null,"abstract":"<p><p>Peptide quantitative structure-activity relationship (PepQSAR) has attracted much attention in the bio- and cheminformatics communities as a well-established computational peptidology strategy to statistically correlate the sequence/structure and activity/function of bioactive peptides (BAPs). In this study, a new concept termed DeepPepQSAR that integrates deep learning into traditional PepQSAR is proposed to quantitatively model, predict, and interpret the BAP universe in an all-in-one manner, that is, massive BAP samples with diverse activity types (i.e. antimicrobial, antiviral, hemolytic, anticancer, antigen, ACE-inhibitory, antioxidant, domain-binding, etc.) are merged into a single all-in-one DeepPepQSAR framework for artificial intelligence (AI)-driven big-data BAP discovery. A novel PepImage map is described to graphically represent both the sequence features of length-varying peptides and the activity types tested for these peptides, which is then fed into a dual-path, single-/multiple-channel convolutional neural network (CNN) for training, developing, and validating DeepPepQSAR regression models. We also practice the CNN-based DeepPepQSAR methodology on extrapolative navigation across a large-scale molecular diversity space covering billions of peptidic fragment candidates generated systematically from various food-derived proteins (FDPs) for AI-driven antimicrobial food peptide (AMFP) and antihypertensive food peptide (AHFP) discovery. Consequently, 14 AMFP peptides and 10 AHFP peptides are determined to have good antibacterial and ACE-inhibitory profiles, in which 4 and 2 peptides exhibit high potencies, respectively.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417672","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 potent biphenyl-macolacin derivatives through a lysine-T/CDHA iterative scanning (LTIS) strategy followed by chemical ligation-based modifications.","authors":"Xiaoshu Jing, Kaixuan Song, Biyao Liu, Kang Jin","doi":"10.1007/s11030-026-11663-7","DOIUrl":"https://doi.org/10.1007/s11030-026-11663-7","url":null,"abstract":"<p><p>The antibiotic biphenyl-macolacin (Bip-macolacin) is a promising drug candidate with good activity against Gram-negative bacteria including several colistin-resistant pathogens, providing a potential structural motif for developing novel antibiotics. Considering the importance of efficient and effective structural optimization in medicinal chemistry studies, we herein report a lysine-T/CDHA iterative scanning (LTIS) strategy to produce Bip-macolacin analogues and identify the modifiable sites, followed by structural derivatization through chemical ligation chemistry. Using this approach, four classes of Bip-macolacin derivatives were prepared conveniently, several of which exhibited antibacterial activities comparable or better than those of Bip-macolacin. The efficacy of representative analogues 5, 18, and 46 was also demonstrated by the resistance development evaluation and hemolysis assay. From this study, a systematic structure-activity relationship of Bip-macolacin was established as a reference for further development of Bip-macolacin-based antibiotics. This novel and powerful strategy also provides more opportunities and valuable options for the structural optimization in future research on peptide therapeutics.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148417633","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":"Multicomponent reactions for protein tyrosine phosphatase inhibitors.","authors":"Eyad A H Mohammed, Taoda Shi, Wenhao Hu","doi":"10.1007/s11030-026-11650-y","DOIUrl":"https://doi.org/10.1007/s11030-026-11650-y","url":null,"abstract":"<p><p>Protein tyrosine phosphatases (PTPs) are pivotal regulators of cellular signaling and potential therapeutic targets for cancer, metabolic disorders, and autoimmune diseases. However, the development of PTP inhibitors remains challenging due to the highly conserved catalytic pocket, the poor membrane permeability of phosphotyrosine mimetics, and difficulties in achieving subtype selectivity. Multicomponent reactions (MCRs) offer a powerful solution by enabling the rapid and modular synthesis of structurally diverse, medicinally relevant scaffolds in a one-pot manner. Through the flexible combination of building blocks, MCRs facilitate efficient optimization of polarity, topology, conformational rigidity, and secondary-pocket engagement, thereby accelerating the discovery of selective and drug-like PTP inhibitors. This review provides the first systematic overview of MCR-enabled PTP inhibitor discovery. The discussion is organized according to major MCR strategies and PTP target classes, highlighting how MCR chemistry has been applied to rapidly construct focused libraries, identify novel scaffolds, and optimize potency and selectivity. Key design concepts-including phosphotyrosine mimicry, fragment extension, conformational constraint, and stereochemical control-are critically summarized. Current limitations and emerging opportunities, such as AI-assisted design, virtual screening, and dynamic combinatorial chemistry, are also discussed. By positioning MCRs as design-enabling technologies, this review outlines a practical framework for the development of next-generation PTP inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410085","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 anti-prostate cancer activity evaluation of novel thieno[2,3-b]pyridine derivatives as potential BLM helicase inhibitors.","authors":"Wei Tu, Ningning Zan, Xinyu Liu, Gang Yu, Xiaoping Zeng, Yafang Zhou, Jia Yu, Xueling Meng, Kun Liu, Xiaolin Peng, Sha Cheng, Bixue Xu, Guangcan Xu","doi":"10.1007/s11030-026-11649-5","DOIUrl":"https://doi.org/10.1007/s11030-026-11649-5","url":null,"abstract":"<p><p>Herein, twenty-four novel thieno[2,3-b]pyridine derivatives were rationally designed and synthesized based on a versatile key intermediate. All compounds were evaluated for in vitro antiproliferative activity against PC3 and VCaP prostate cancer cell lines using the MTT assay, and most exhibited good antitumor potency. Compound 9v showed the strongest activity, with IC₅₀ values of 0.49 μM (PC3) and 0.85 μM (VCaP). Mechanistic studies revealed that 9v concentration-dependently induced apoptosis and G0/G1 cell cycle arrest, and suppressed PCa cell migration and invasion in both time- and concentration-dependent manners. Further target validation showed that 9v efficiently inhibited BLM helicase-mediated DNA unwinding with an IC₅₀ of 3.17 μM, outperforming the lead compound TC1. Combined MST and BLI assays verified that 9v exhibited higher binding affinity toward BLM protein than the lead compound TC1 and preferentially interacted with BLM instead of its DNA substrate. Mechanistically, 9v competitively interfered with BLM-DNA substrate interaction to block BLM DNA unwinding function. Molecular docking revealed that 9v stably bound to key amino acid residues in the BLM RQC domain, consistent with the binding characteristics of reported BLM inhibitors. In conclusion, 9v displays superior antiproliferative and BLM helicase inhibitory activities compared with TC1, representing a promising lead compound for the development of novel BLM-targeted anti-prostate cancer agents.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410100","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}