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{"title":"Discovery of novel cinnamic acid derivatives with anti-Helicobacter pylori mechanism.","authors":"Yonglian Li, Xiuya Ye, Yuan'e Yang, Wenfeng Liu, Suqing Zhao","doi":"10.1007/s11030-026-11651-x","DOIUrl":"10.1007/s11030-026-11651-x","url":null,"abstract":"<p><p>With the efficacy of standard anti-Helicobacter pylori (Hp) regimens declining due to increasing resistance and adverse effects, the development of safer alternatives is imperative. Inspired by the bioactivity of cinnamic acid, a series of cinnamic acid derivatives was designed and screened for their activity against Hp. Among the synthesized derivatives, compound 23, bearing a dinitro group, exhibited potent anti-Hp activity with a minimum inhibitory concentration (MIC) of 4 µM, demonstrating a two-fold greater potency than metronidazole. Meanwhile, the quinoline-containing derivative (compound 25) showed activity comparable to that of metronidazole. Compound 23 was highly specific for Hp, showing no efficacy against other gastrointestinal pathogens (MIC > 128 µM). The mechanistic studies indicated that compound 23 could cause severe cellular damage (vesicle formation, swelling, and plasmolysis), disrupt biofilms, and inhibit urease, thereby blocking ammonia cloud formation. Furthermore, it effectively suppressed the production and the overexpression of inflammatory mediators in RAW264.7 cells. Therefore, these results suggested that compound 23 was a viable lead candidate for the treatment of Hp infection.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"9943-9959"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148389783","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":"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":"10103-10131"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","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":"Copper-catalyzed desulfurative guanylation of amines using CS₂ as a C1 synthon.","authors":"Pinyong Zhong, Kangqiang Lu, Jin-Biao Liu","doi":"10.1007/s11030-026-11660-w","DOIUrl":"10.1007/s11030-026-11660-w","url":null,"abstract":"<p><p>Guanidine compounds constitute a significant class of nitrogen-containing organic molecules with widespread applications in medicinal chemistry and synthetic science. Herein, we report a copper-catalyzed desulfurization strategy for the efficient synthesis of guanidine derivatives directly from amines and carbon disulfide. This transformation hinges on the in situ generation of a thiourea intermediate of a thiourea intermediate, followed by copper-catalyzed desulfurization to afford a highly reactive carbodiimide intermediate, with molecular oxygen serving as a green terminal oxidant in the catalytic cycle. In contrast to conventional methodologies relying on hypervalent iodine reagents, this protocol circumvents the use of strong oxidants and exhibits broad substrate scope and excellent functional group tolerance. Notably, this approach is amenable not only to the construction of structurally diverse and complex guanidine derivatives but also to the late-stage functionalization of drug-related molecules.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"10251-10258"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434738","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":"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":"10733-10753"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","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":"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":"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":"10891-10915"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","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":"Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis.","authors":"Hafs Essaadi, Abdelfateh Chourir, Jouhaina Kourou, Farah Makhloufi, Ouissal Hachlaf, Aya Abidou, Saber Boutayeb, Rachid Eljaoudi, Lahcen Belyamani, Azeddine Ibrahimi, Mohammed Hakmi, Naima El Hafidi","doi":"10.1007/s11030-026-11704-1","DOIUrl":"10.1007/s11030-026-11704-1","url":null,"abstract":"<p><p>Human rhinoviruses (HRVs) are major causative agents of the common cold and are strongly associated with asthma exacerbation, highlighting the need for effective antivirals. Because the viral 3 C protease (3Cpro) is essential for polyprotein processing and is highly conserved among HRV species, it represents an attractive therapeutic target. In this study, we performed structure-based virtual screening of 49,437 compounds against HRV-C 3Cpro using AutoDock Vina, followed by ADMET-based prioritization of the top hits. Docking analysis identified two lead compounds that favorably occupied the catalytic pocket and formed stabilizing interactions with key residues, including His40, Glu71, and Cys147, in comparison with the reference inhibitor rupintrivir. In 200-ns molecular dynamics simulations, both compounds stabilized the protease relative to the apo form, with compound 1 showing the lowest protein RMSD (1.21 ± 0.22 Å) and compound 2 producing the lowest Cys147 RMSF (0.48 Å). Both ligands also displayed lower ligand RMSD values than rupintrivir (compound 1: 1.49 ± 0.78 Å; compound 2: 2.01 ± 0.39 Å; rupintrivir: 4.83 ± 0.89 Å), indicating more persistent binding within the active site. MM/PBSA analysis further supported stronger effective binding for compound 1 (-23.59 ± 6.87 kcal/mol) and compound 2 (-25.25 ± 5.75 kcal/mol) than for rupintrivir (-19.43 ± 4.46 kcal/mol). Taken together, these results identify compounds 1 and 2 as promising non-covalent HRV 3Cpro inhibitor candidates for further experimental validation.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"11005-11020"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148917960","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 structural dynamics and immunoinformatic analysis of EIEC IpaH_4.5 targeting immunoprophylaxis design against bacillary dysentery.","authors":"Pinkan Sadhukhan, Nibedita Mahata","doi":"10.1007/s11030-026-11667-3","DOIUrl":"10.1007/s11030-026-11667-3","url":null,"abstract":"<p><p>Bacillary dysentery continues as a latent global health challenge, mainly affecting infants in developing nations. The lack of a licensed vaccine and spread of antimicrobial resistance, underlines the need for an alternative immunoprophylactic management. The present study was to identify a nasal subunit vaccine candidate against bacillary dysentery. For this, the virulence-associated EIEC IpaH_4.5 protein, also conserved across pathogenic Shigella species, was selected as the antigenic source. A 29-mer protective antigenic peptide (PAP) (⁴⁴TTTENRIQAVRLLKICLDTREPVLNLSLL⁷²) was identified within its N-terminal region, consisted of overlapping two B-cell, one CTL, and seven HTL epitopes. Both, T-cell epitopes were screened for strong HLA-binding potential (IC₅₀ < 500 nM). Screened HTL epitopes positively induced IFN-γ, IL-4, and IL-10 and were non-homologous to human. Epitope clustering underscored the PAP region, having 99.68% world-wide population coverage. The 3D model of EIEC IpaH_4.5 was predicted using ab-initio method (c-score: 0.44). Whereas, homology modelling was utilized to model the extracellular human Toll-like receptors (TLR1/2/4/5/6) corresponding to pulmonary macrophages. A 100 ns molecular dynamics simulation revealed a stable RMSD (Backbone) plot with an average fluctuation of 0.92 ± 0.05 nm for IpaH_4.5 and TLR 4 complex. MM/PBSA analysis yielded an average ∆G: - 76.65 ± 8.12 kcal/mol, while MM/GBSA analysis produced a value of - 52.40 ± 6.45 kcal/mol, indicating an energetically favourable complex. In-silico immune titers of the IpaH_4.5, was seen to activate both humoral and cellular immunity. Finally, in-silico cloning aided theoretical expression feasibility of the EIEC IpaH_4.5. However, real-world experimental validation would be needed for evaluating its protective immunogenicity.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"10271-10287"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468184","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 potential MAP3K8 hit molecules: a comprehensive structure- and ligand-based studies.","authors":"Md Ataul Islam, Aaherasha Iqbal, Mohammad Ajmal Ali, Rupesh Chikhale, Md Lutful Islam, Mohammad Abul Farah","doi":"10.1007/s11030-026-11696-y","DOIUrl":"10.1007/s11030-026-11696-y","url":null,"abstract":"<p><p>MAP3K8, also known as COT or Tpl2, is a serine/threonine kinase that plays a pivotal role in regulating the MAPK signaling pathway and pro-inflammatory cytokine production, making it an attractive therapeutic target for inflammatory disorders and cancer. In this study, an integrated ligand- and structure-based virtual screening workflow was used to identify potential MAP3K8 inhibitors. A pharmacophore model derived from the co-crystal ligand bound to the COT kinase domain was used to screen the ChEMBL, PubChem, and ZINC databases, yielding 20,566 unique molecules. Sequential filtering through RMSD and binding-score thresholds, RDKit-based similarity searching, AutoDock Vina molecular docking, ADMET evaluation, and ChemMASTER-guided clustering reduced the dataset to four structurally diverse lead candidates designated as PM1, PM2, PM3, and PM4. All four molecules preserved the canonical hinge-binding pharmacophore of the reference ligand and exhibited binding energies between - 9.0 and - 10.2 kcal/mol, ranking more favourably than the co-crystal ligand with - 8.6 kcal/mol. The 200 ns molecular dynamics (MD) simulations confirmed the structural stability of the complexes, supported by favourable RMSD, RMSF, RoG, SASA, hydrogen-bond, PCA, and free energy landscape profiles. MM-GBSA calculations revealed that the binding free energies of PM2, PM3, and PM4 were - 42.12, - 38.46, and - 36.96 kcal/mol, respectively, outperformed the co-crystal ligand with - 34.36 kcal/mol, while FEP analysis identified PM3 with - 14.59 kcal/mol as the most thermodynamically favourable test compound, followed by PM2 and PM4. The convergence of docking, dynamics, and free-energy results prioritized PM2, PM3, and PM4 as promising MAP3K8 hit candidates, which require further experimental validation and lead optimization.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"10567-10587"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148719502","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 characterization of a hydrophobic tagging EGFR degrader for the treatment of non-small-cell lung cancer.","authors":"Mo Li, Chengye Wang, Xingting Wang, Haotianyu Zhu, Yu Bai","doi":"10.1007/s11030-026-11669-1","DOIUrl":"10.1007/s11030-026-11669-1","url":null,"abstract":"<p><p>EGFR is a validated therapeutic target for non-small cell lung cancer (NSCLC). Herein, we designed and synthesized a series of gefitinib-based hydrophobic tagging (Hyt) degraders to induce EGFR degradation. Among them, B3 was identified as an active degrader, with DC₅₀ values of 0.61 and 0.73 μM in the EGFR-del19 NSCLC cells HCC827 and PC9, respectively, and DT₅₀ values of 9.2 and 13.7 h. Mechanistic studies showed that B3 accelerated EGFR turnover and revealed a degradation process involving the ubiquitin-proteasome system and the autophagy-lysosome pathway. B3 also suppressed EGFR downstream signaling and inhibited the proliferation of HCC827 and PC9 cells, with IC₅₀ values of 0.17 and 0.25 μM, respectively. In an HCC827 xenograft model, B3 significantly inhibited tumor growth and reduced EGFR levels in tumor tissues without obvious toxicity. These results identify B3 as an active EGFR hydrophobic tag degrader and a useful compound for further investigation in NSCLC models harboring EGFR del19.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"10289-10302"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148468187","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":"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":"10917-10944"},"PeriodicalIF":4.3,"publicationDate":"2026-12-01","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}