Molecular Diversity最新文献_第2页

Discovery of aloe-emodin as an antidepressant agent by targeting RANBP9. 以RANBP9为靶点的芦荟大黄素抗抑郁药物的发现。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-29 DOI: 10.1007/s11030-026-11688-y

Yangyang Yang, Dezhong Sun, Qingyun Jia, Bing Zhu, Huaiqing Lv, Fengyuan Che

{"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":"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.","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}

引用次数: 0

Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors. 利用机器学习、对接和分子动力学对CDK4/6双抑制剂进行虚拟筛选。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-29 DOI: 10.1007/s11030-026-11716-x

Yuxi Wang, Linxia Fang, Quanfang Liu, Zhiwei Zhang, Cong Xu, Yihui Jiang

{"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":"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.","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}

引用次数: 0

Machine learning-driven drug repurposing and computational validation for Nipah virus. 尼帕病毒的机器学习驱动药物再利用和计算验证。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11708-x

Shivangi Sharma, Pragya D Yadav, Sarah Cherian

{"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":"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.","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}

引用次数: 0

Integrated multi-omics, machine learning, network toxicology, and molecular docking reveal potential mechanisms underlying methyl 4-hydroxybenzoate-associated breast cancer. 综合多组学、机器学习、网络毒理学和分子对接揭示4-羟基苯甲酸甲酯相关乳腺癌的潜在机制。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11712-1

Chunhong Li, Xin Zeng, Yuhua Mao

{"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":"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.","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}

引用次数: 0

Natural products mediate ferroptosis and immune microenvironment-linked sensitization in osteosarcoma: from chemotherapy resistance to combined therapeutic transformation. 天然产物介导骨肉瘤中的铁上吊和免疫微环境相关致敏：从化疗耐药到联合治疗转化

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11707-y

Yuwen Dai, Wanjun Ding

{"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":"https://doi.org/10.1007/s11030-026-11707-y","url":null,"abstract":"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.","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}

引用次数: 0

Interpretable QSAR modelling for PPAR-γ agonist prediction by integrating a stacking strategy, docking, and MD simulations. 通过整合堆叠策略、对接和MD模拟，为PPAR-γ激动剂预测建立可解释的QSAR模型。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11700-5

Phasit Charoenkwan, Ittipat Meewan, Nalini Schaduangrat, Watshara Shoombuatong

{"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":"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.","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}

引用次数: 0

Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations. 通过基于大规模机器学习的虚拟筛选和分子动力学模拟鉴定新型血清素转运体（SERT）抑制剂。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11705-0

Rujia Zhao, Tianzhu Shen, Tongzhou Huang

{"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":"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.","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}

引用次数: 0

Computational discovery of HIF-1α/VHL protein-protein interaction inhibitors for hypoxic cell protection. HIF-1α/VHL蛋白-蛋白相互作用抑制剂对缺氧细胞保护的计算发现。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-28 DOI: 10.1007/s11030-026-11706-z

Binglin Huang, Bijuan Lin, Xiao Chen, Yifan Zhu, Na Li, Bin Zheng, Weiwei Zheng, Xin Xue, Maobai Liu

{"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":"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.","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}

引用次数: 0

Design, synthesis and biological evaluation of quinazoline-carbazole derivatives as dual TXNIP/DYRK1A inhibitors for the treatment of type 2 diabetes mellitus. 喹唑啉-咔唑衍生物TXNIP/DYRK1A双抑制剂治疗2型糖尿病的设计、合成及生物学评价

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-22 DOI: 10.1007/s11030-026-11690-4

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

{"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":"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.","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}

引用次数: 0

Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner. 超复古：超网络引导的偏好条件下的逆合成规划器。

IF 4.3 2区化学

Molecular Diversity Pub Date : 2026-08-20 DOI: 10.1007/s11030-026-11694-0

Jingwen Wang, Shuan Liu, Shaoye Zhang, Jianbo Qiao, Guanhe Li, Hanjun Zhao, Xiangxiang Zeng, Jinjin Li, Changhang Lin, Chen Su, Leyi Wei

{"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":"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.","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}

引用次数: 0