化学最新文献

{"title":"Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.","authors":"Rima Bharadwaj, Amer M Alanazi, Vivek Dhar Dwivedi, Sarad Kumar Mishra","doi":"10.1007/s11030-024-11079-1","DOIUrl":"10.1007/s11030-024-11079-1","url":null,"abstract":"<p><p>Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and molecular dynamics to identify and validate novel BCL-2 inhibitors from the ChEMBL database. Starting with 836 BCL-2 inhibitors, we performed ADME and Lipinski's Rule of Five (RO5) filtering, clustering, maximum common substructure (MCS) analysis, and machine learning models (Random Forest, SVM, and ANN), yielding a refined set of 124 compounds. Among these, 13 compounds within the most common substructure (MCS1) cluster showed promising features and were prioritized. A docking-based re-evaluation highlighted four lead compounds-ChEMBL464268, ChEMBL480009, ChEMBL464440, and ChEMBL518858-exhibiting notable binding affinities. Although a reference molecule outperformed in docking, molecular dynamics (MD), and binding energy analyses, it failed ADME and Lipinski criteria, unlike the selected leads. Further validation through MD simulations and MM/GBSA energy calculations confirmed stable binding interactions for the leads, with ChEMBL464268 showing the highest stability and binding affinity (ΔGtotal = - 80.35 ± 11.51 kcal/mol). Free energy landscape (FEL) analysis revealed stable energy minima for these complexes, underscoring conformational stability. Despite moderate activity (pIC₅₀ values from 4.3 to 5.82), the favorable pharmacokinetic profiles of these compounds position them as promising BCL-2 inhibitor leads, with ChEMBL464268 emerging as the most promising candidate for further CLL therapeutic development.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3233-3252"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142942361","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":"General structure-activity relationship models for the inhibitors of Adenosine receptors: A machine learning approach.","authors":"M Janbozorgi, S Kaveh, M S Neiband, A Mani-Varnosfaderani","doi":"10.1007/s11030-024-11096-0","DOIUrl":"10.1007/s11030-024-11096-0","url":null,"abstract":"<p><p>Adenosine receptors (A₁, A_2a, A_2b, A₃) play critical roles in cellular signaling and are implicated in various physiological and pathological processes, including inflammations and cancer. The main aim of this research was to investigate structure-activity relationships (SAR) to derive models that describe the selectivity and activity of inhibitors targeting Adenosine receptors. Structural information for 16,312 inhibitors was collected from BindingDB and analyzed using machine learning methods. 450 molecular descriptors were calculated for each molecule and compounds were classified based on their activity levels and therapeutic targets. The variable importance in projection (VIP) algorithm identified key discriminating features. Classification models were built using supervised Kohonen networks (SKN) and counter-propagation artificial neural networks (CPANN) algorithms. Model validity was assessed via cross-validation, applicability domain analysis, and test sets. These models were then used to screen a random subset of 2 million molecules from the ZINC database. Three descriptors-hydrophilic factor (Hy), ratio of multiple path count over path count (PCR), and asphericity (ASP)-were identified as critical for discriminating active and inactive inhibitors. SKN models exhibited high sensitivity (0.88-0.99) and yielded an average area under the curve (AUC) of 0.922 for virtual screening. This study aimed to enhance the development of highly selective Adenosine receptor ligands for diverse therapeutic applications by identifying critical molecular features specific to each isoform.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3253-3272"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998221","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":"Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.","authors":"Alireza Poustforoosh","doi":"10.1007/s11030-025-11114-9","DOIUrl":"10.1007/s11030-025-11114-9","url":null,"abstract":"<p><p>The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3293-3303"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998058","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":"iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer.","authors":"Ravishankar Jaiswal, Girdhar Bhati, Shakil Ahmed, Mohammad Imran Siddiqi","doi":"10.1007/s11030-024-11055-9","DOIUrl":"10.1007/s11030-024-11055-9","url":null,"abstract":"<p><p>Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promising therapeutic target, often hyperactivated in TNBC, contributing to uncontrolled growth and cancer cell formation. We have proposed an interpretable deep convolutional neural network prediction (iDCNNPred) system using 2D molecular images to classify bioactivity and identify potential PI3Ka inhibitors. We built Custom-DCNN models and pre-trained models such as AlexNet, SqueezeNet, and VGG19 by using the Bayesian optimization algorithm, and found that our Custom-DCNN model performed better than a pre-trained model with lower complexity and memory usage. All top-performed models were screened with the Maybridge Chemical library to find predictive hit molecules. The screened molecules were further evaluated for protein-ligand interaction with molecular docking and finally 12 promising hits were shortlisted for biological validation using in-vitro PI3K inhibition studies. After biological evaluation, 4 potent molecules with different structural moieties were identified, and these molecules present new starting scaffolds for further improvement in terms of their potency and selectivity as PI3K inhibitors with the help of medicinal chemistry efforts. Furthermore, we also showed the significance of the interpretation and visualization of the model's predictions by the Grad-CAM technique, enhancing the robustness, transparency, and interpretability of the model's predictions. The data and script files and prediction run of models used for this study to reproduce the experiment are available in the GitHub repository at https://github.com/ravishankar1307/iDCNNPred.git .</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3077-3100"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142794296","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":"Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.","authors":"Aga Basit Iqbal, Tariq Ahmad Masoodi, Ajaz A Bhat, Muzafar A Macha, Assif Assad, Syed Zubair Ahmad Shah","doi":"10.1007/s11030-025-11133-6","DOIUrl":"10.1007/s11030-025-11133-6","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The viability of cells and the integrity of the genome depend on the detection and repair of damaged DNA through intricate mechanisms. Cancer treatment employs chemotherapy or radiation therapy to eliminate neoplastic cells by causing substantial damage to their DNA. In many cases, improved DNA repair mechanisms lead to resistance to these medicines; therefore, it is essential to expand efforts to develop drugs that can sensitise cells to these treatments by inhibiting the DNA repair process. Multiple studies have demonstrated a correlation between the overexpression of Apurinic/Apyrimidinic Endonuclease (APE1), the primary mammalian enzyme responsible for excising apurinic or apyrimidinic sites in DNA, and the resistance of cells to cancer therapies; in contrast, APE1 downregulation increases cellular susceptibility to DNA-damaging agents. Thus, the effectiveness of existing therapies can be improved by promoting the targeted sensitization of cancer cells while protecting healthy cells. The current study aims to employ explainable artificial intelligence (XAI) to enhance the accuracy and reliability of machine learning models for the prediction of APE1 inhibitors. Various ML-based regression models are employed to predict the pIC50 value of different medicines. Bayesian optimization and the Permutation Feature Importance (PFI) approach are employed to determine the best hyperparameters of machine learning models and to discover the most significant features for recognizing drug candidates that target APE1 enzymes, respectively. To acquire comprehensive elucidations for the predictive models in our research, two XAI methodologies, namely SHAP and LIME, are used. The SHAP analysis reveals that the features 'C1SP2' and 'ASP-2' are essential in influencing the model's predictions. The SHAP values demonstrate variability for features such as 'maxHBint2' and 'GATS1s,' signifying that their impact is dependent on specific instances within the dataset. The LIME study corroborates these findings, demonstrating that 'C1SP2' and 'ASP-2' are the most significant positive contributors, whereas features like 'SHCHnX,' 'nHdCH2,' and 'GATS1s' result in a decrease in the predicted values. Due to the limited sample size of the APE1 dataset, direct training on this dataset posed challenges in model generalization and reliability. To overcome this limitation, the BACE-1 dataset is leveraged for model training, enabling the ML models to learn from a more extensive and diverse chemical space. Among the tested algorithms, XGBoost demonstrated superior predictive performance, achieving R&lt;sup&gt;2&lt;/sup&gt; = 0.890, MAE = 0.186, and RMSE = 0.245, significantly surpassing state-of-the-art methods, such as LightGBM and QSAR-ML, which attained R&lt;sup&gt;2&lt;/sup&gt; scores of 0.798 and 0.630, respectively. These results highlight the robustness of our approach, demonstrating its enhanced generalization capability and superior predictive accuracy compared to existing methodologies.&lt;/","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3371-3390"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466396","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":"Overmedialization of Thyroplasty Implants and the Role for Anterior-Posterior Laryngeal Compression as a Diagnostic Tool.","authors":"Arnav A Shah, Keonho A Kong, Heather A Yeakel, Aaron J Jaworek, Robert T Sataloff","doi":"10.1177/01455613221136676","DOIUrl":"10.1177/01455613221136676","url":null,"abstract":"","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"478-479"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40671062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Clinical Prediction Model to Diagnose Sinonasal Inverted Papilloma Based on Computed Tomography Features and Clinical Characteristics.","authors":"Zengxiao Zhang, Longgang Yu, Jiaxin Jiang, Lin Wang, Shizhe Zhou, Dapeng Hao, Yan Jiang","doi":"10.1177/01455613221134421","DOIUrl":"10.1177/01455613221134421","url":null,"abstract":"<p><p><b>Objectives:</b> Sinonasal inverted papilloma (SNIP) is one of the most common benign tumors of the nasal cavity and sinuses and is at risk for recurrence and malignant transformation. It is crucial to precisely predict SNIP before surgery to determine the optimal surgical technique and prevent SNIP recurrence. This study aimed to evaluate the diagnostic value of computed tomography (CT) features and SNIP clinical characteristics and to develop and validate a clinically effective nomogram. <b>Methods:</b> Here, 267 patients with SNIP and 273 with unilateral chronic rhinosinusitis with/without nasal polyps were included. Patient's demographic and clinical characteristics (i.e., gender, age, nasal symptoms, history of sinus surgery, smoking, and alcohol dependence) and CT features (i.e., lobulated/wavy edge, air sign, focal hyperostosis, diffuse hyperostosis, focal osseous erosion, and CT values) were recorded. Independent risk factors were screened using logistic regression analysis. A nomogram model was developed and validated. <b>Results:</b> Logistic regression analysis showed that age, facial pain/headache, history of sinus surgery, lobulated/wavy edge, air sign, focal hyperostosis, focal osseous erosion, and CT values were independent predictors of SNIP. A nomogram comprising these 8 independent risk factors was established. The area under the curve (AUC) for the training set was .960 (95% CI, .942-.978) and the AUC for the validation set was .951 (95% CI, .929-.971). <b>Conclusion:</b> The obtained results suggested that the nomogram based on age, facial pain/headache symptoms, history of sinus surgery, and CT characteristics had an excellent diagnostic value for SNIP.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":" ","pages":"NP540-NP549"},"PeriodicalIF":16.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40560805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of endogenous SUMOylation sites by click chemistry-based proteomics.","authors":"Ke Ma, Sixian Chen, Jun Zhang, Xinglong Jia, Rufeng Fan, Mingjun Li, Li Dong, Minjia Tan, Wensi Zhao, Dong Xie","doi":"10.1007/s00216-025-05957-2","DOIUrl":"10.1007/s00216-025-05957-2","url":null,"abstract":"<p><p>SUMOylation, an essential ubiquitin-like modification in eukaryotes, plays vital roles in both physiological and pathological regulation, positioning it as a promising therapeutic target. However, the low abundance of SUMOylation and the high enzymatic activity of sentrin/SUMO-specific proteases (SENPs) complicate the identification of endogenous sites. In this study, we integrated click chemistry, acid cleavage, and SUMOylated peptide enrichment into the workflow and developed a promising methodology for system-wide identification of SUMOylation sites. In total, we identified 962 endogenous SUMOylation sites in HEK293T cells under heat shock conditions, which showed good complementarity with previous studies. Our approach uncovered 105 potentially new sites, including SSRP1-K248/K319/K612, DHX9-K806, and ILF3-K241, which showed high conservation and were located in functionally important domains. The overlap between SUMOylation sites and the known ubiquitination or acetylation sites suggested the potential PTM crosstalks. KEGG analysis further suggested SUMOylated proteins were associated with carbon metabolism and biosynthesis of amino acids pathways. Collectively, this study provides a valuable tool for systematically identifying SUMOylation sites, advancing further biological understanding of their dynamic regulatory networks and pathophysiological mechanisms.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4419-4433"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144300862","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":"Development and characterization of angiotensin I certified reference materials.","authors":"Fanyu Deng, Ruiqi Wang, Kaifa Liu, Liqing Wu, Rui Su, Yahui Liu","doi":"10.1007/s00216-025-05955-4","DOIUrl":"10.1007/s00216-025-05955-4","url":null,"abstract":"<p><p>Angiotensin I (ANGI) plays an important role in regulating blood pressure and maintaining homeostasis; the accurate analysis of its levels is beneficial in the clinical diagnosis and treatment of diseases. Despite the existence of several detection techniques for ANGI, a \"yardstick\" to evaluate the results of different detection techniques is still lacking. To ensure the reliability of ANGI tests along with the accuracy and comparability of the analytical results, natural and isotope-labeled ANGI certified reference materials were characterized, and their purities were assessed by mass balance and quantitative nuclear magnetic resonance methods. The moisture content of the samples was determined using the Karl Fischer method, anion content was measured via ion chromatography, and inorganic elements were detected via inductively coupled plasma mass spectrometry. In the quantitative nuclear magnetic resonance method, maleic acid was used as a quantitative internal standard, and the final values of natural and isotope-labeled ANGI were determined on the basis of their quantitative peaks. The final purity was verified by isotope dilution mass spectrometry. The natural and labeled ANGI purities were (0.8511 ± 0.041) g/g and (0.8696 ± 0.042) g/g, respectively. Furthermore, the abundance of double-labeled ANGI-which refers to the labeling rate of ¹³C and ¹⁵N in labeled ANGI-was determined via high-resolution liquid-mass spectrometry, affording 98.98% ¹³C and 99.43% ¹⁵N. This study establishes high-purity natural and isotope-labeled ANGI reference materials, ensuring accurate, comparable measurements for clinical diagnostics and biomarker research. It also supports method validation, quality control, and interlaboratory comparisons through advanced analytical techniques.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4395-4406"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324089","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":"NO-CO Monitoring Technique Using Ultraviolet Absorption Spectroscopy and Tunable Diode Laser Absorption Spectroscopy in High-Temperature and High-Pressure.","authors":"Wangzheng Zhou, Xiaowei Qin, Zhenzhen Wang, Yoshihiro Deguchi, Daotong Chong, Junjie Yan","doi":"10.1177/00037028251324196","DOIUrl":"10.1177/00037028251324196","url":null,"abstract":"<p><p>The single parameter detection of temperature (H₂O) is no longer sufficient for the absorption combustion diagnosis. There is a huge demand for simultaneous computed tomography (CT) diagnosis of multi-parameters. This paper studied CO and NO, two representative combustion products based on tunable diode laser absorption spectroscopy (TDLAS) and ultraviolet absorption spectroscopy (UVAS). Different from the research on low detection limits, the absorbance needs to be corrected in high-temperature and high-pressure conditions due to the equipment performance of the CT system. A high-temperature and high-pressure chamber system was applied for the basic absorbance experiment. The corrected absorbance databases of 2325.2/2326.8  nm for CO, and 215/226  nm band for NO were established. The corrected absorbance databases were first compared with the HITRAN and ExoMol databases. The accuracy of the corrected databases was also analyzed by standard gas with 1D detection in the high-temperature and high-pressure chamber and two-dimensional (2D) reconstruction in a customed CT cell. The maximum CO mean relative error (MRE) of the 2D results is 2.75% while the maximum NO MRE is 4.99%. This study provides a basis for research on the CO and NO distribution in high-temperature and high-pressure combustion fields.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1206-1217"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143656106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}