化学最新文献

{"title":"Characterizing Variability in Non-Invasive Hydration Monitoring Using Raman Spectroscopy.","authors":"Anna S Rourke-Funderburg, Laura J Elstub, Trevor Voss, Richard L Liao, Laura E Masson, Anita Mahadevan-Jansen","doi":"10.1177/00037028241307043","DOIUrl":"10.1177/00037028241307043","url":null,"abstract":"<p><p>Significant dehydration can increase thermoregulatory and cardiovascular strain and impair physical and cognitive performance. Despite these negative effects, there are currently no objective, non-invasive tools to monitor systemic hydration. Raman spectroscopy is an optical modality with the potential to fill this gap because it is sensitive to water, provides results quickly, and can be applied non-invasively. In this work, high wavenumber Raman spectroscopy has been developed toward detection of systemic hydration via validation with tissue-mimicking phantoms, followed by three in vivo feasibility studies to investigate the relationship between spectral features and systemic hydration. The area under the curve (AUC) of the water bands and the ratio of water bands to CH bands are Raman-derived metrics that can be used to describe systemic hydration. Here, we determined a trend in decreasing water bands AUC after exercise, although the magnitude of the change was highly variable. In investigating the sources of variability, we identified significant inter-subject variability and a failure of current clinical standards to benchmark our developed technique against. Despite the high variability, we found that multiple anatomical locations were suitable for collecting the spectral measurements. While the high degree of variability may confound the use of Raman spectroscopy for non-invasive hydration monitoring, when implementing additional study standardization, significant differences (<i>p</i> <.05) in spectral metrics can be identified before and after exercise. Raman spectroscopy can allow for rapid, non-invasive detection of systemic hydration, which would improve routine hydration monitoring and reduce the incidence of negative side effects associated with dehydration.</p>","PeriodicalId":8253,"journal":{"name":"Applied Spectroscopy","volume":" ","pages":"1228-1241"},"PeriodicalIF":2.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142891707","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}

{"title":"Database of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and hyperspectral imaging (HSI) spectra of pigments and dyes for historical document analysis.","authors":"Anna Sofia Reichert, Ana Belén López-Baldomero, Francisco Moronta-Montero, Ana López-Montes, Eva María Valero, Carolina Cardell","doi":"10.1007/s00216-025-05948-3","DOIUrl":"10.1007/s00216-025-05948-3","url":null,"abstract":"<p><p>Characterizing pigments and dyes in historical manuscripts is challenging due to the fragility of materials, the complex composition of low-concentration elements, and sampling limitations. Consequently, complementary non-invasive analytical techniques and non-contact measurement methods are often required. This study presents the most comprehensive spectral database to date, combining diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and hyperspectral imaging (HSI) to aid in identifying pigments, dyes, and binders historically used in illuminated and decorated manuscripts. A total of 156 painting mock-ups were created using traditional techniques, incorporating variations in binders, pigment particle sizes, support types, surface roughness, and application methods. Spectral imaging was performed in the visible and near infrared (VNIR) and short-wave infrared (SWIR) regions, while DRIFTS analysis covered the middle wave infrared (MWIR) region. For DRIFTS, both contact and non-contact measurements were tested. Using the samples in the database, the influence of binder, support, and grain size on the sample spectra and color were analyzed and discussed. This database facilitates pigment and dye identification using DRIFTS or HSI data independently or in combination through data fusion, applying techniques ranging from direct spectral comparison to advanced methods such as machine learning and spectral unmixing. By making this database publicly available, the study underscores the value of DRIFTS and HSI in identifying painting materials and contributes to the preservation of historical manuscripts.</p>","PeriodicalId":462,"journal":{"name":"Analytical and Bioanalytical Chemistry","volume":" ","pages":"4351-4372"},"PeriodicalIF":3.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and validation of oxidative stress-related diagnostic markers for recurrent pregnancy loss: insights from machine learning and molecular analysis.","authors":"Hui Hu, Li Yu, Yating Cheng, Yao Xiong, Daoxi Qi, Boyu Li, Xiaokang Zhang, Fang Zheng","doi":"10.1007/s11030-024-10947-0","DOIUrl":"10.1007/s11030-024-10947-0","url":null,"abstract":"<p><p>It has been recognized that oxidative stress (OS) is implicated in the etiology of recurrent pregnancy loss (RPL), yet the biomarkers reflecting oxidative stress in association with RPL remain scarce. The dataset GSE165004 was retrieved from the Gene Expression Omnibus (GEO) database. From the GeneCards database, a compendium of 789 genes related to oxidative stress-related genes (OSRGs) was compiled. By intersecting differentially expressed genes (DEGs) in normal and RPL samples with OSRGs, differentially expressed OSRGs (DE-OSRGs) were identified. In addition, four machine learning algorithms were employed for the selection of diagnostic markers for RPL. The Receiver Operating Characteristic (ROC) curves for these genes were generated and a predictive nomogram for the diagnostic markers was established. The functions and pathways associated with the diagnostic markers were elucidated, and the correlations between immune cells and diagnostic markers were examined. Potential therapeutics targeting the diagnostic markers were proposed based on data from the Comparative Toxicogenomics Database and ClinicalTrials.gov. The candidate biomarker genes from the four models were further validated in RPL tissue samples using RT-PCR and immunohistochemistry. A set of 20 DE-OSRGs was identified, with 4 genes (KRAS, C2orf69, CYP17A1, and UCP3) being recognized by machine learning algorithms as diagnostic markers exhibiting robust diagnostic capabilities. The nomogram constructed demonstrated favorable predictive accuracy. Pathways including ribosome, peroxisome, Parkinson's disease, oxidative phosphorylation, Huntington's disease, and Alzheimer's disease were co-enriched by KRAS, C2orf69, and CYP17A1. Cell chemotaxis terms were commonly enriched by all four diagnostic markers. Significant differences in the abundance of five cell types, namely eosinophils, monocytes, natural killer cells, regulatory T cells, and T follicular helper cells, were observed between normal and RPL samples. A total of 180 drugs were predicted to target the diagnostic markers, including C544151, D014635, and CYP17A1. In the validation cohort of RPL patients, the LASSO model demonstrated superiority over other models. The expression levels of KRAS, C2orf69, and CYP17A1 were significantly reduced in RPL, while UCP3 levels were elevated, indicating their suitability as molecular markers for RPL. Four oxidative stress-related diagnostic markers (KRAS, C2orf69, CYP17A1, and UCP3) have been proposed to diagnose and potentially treat RPL.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"2881-2897"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142118726","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":"Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.","authors":"Qinghe Hou, Yan Li","doi":"10.1007/s11030-024-11061-x","DOIUrl":"10.1007/s11030-024-11061-x","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifactorial pathophysiology of AD, monotargeted agents can only alleviate symptoms but not cure AD. Acetylcholinesterase (AChE) and Monoamine oxidase B (MAO-B) are two key targets in the treatment of AD, molecules that inhibiting both targets are considered promising avenue to develop more effective AD therapies. In the present work, a dual inhibition dataset containing 449 molecules was established, based on which five machine learning algorithms (KNN, SVM, RF, GBDT, and LGBM) four fingerprints (MACCS, ECFP4, RDKitFP, PubChemFP) and DRAGON descriptors were combined to develop 25 classification models in which GBDT paired with ECFP4 and RF paired with PubchemFP achieved the same best performance across multiple metrics (Accuracy = 0.92, F1 Score = 0.94, MCC = 0.81). Moreover, based on the curated bioactivity datasets of AChE and MAO-B, regression models were developed to predict pIC₅₀ values. For the AChE inhibition task, GBDT demonstrated the best performance (RMSE = 0.683, MAE = 0.500, R² = 0.721). The SVM algorithm emerged as the most effective for MAO-B inhibition (RMSE = 0.668, MAE = 0.507, R² = 0.675). The SHAP algorithm was used to interpret the optimal models, identifying and analyzing the key substructures and properties for both dual-target and single-target inhibitors. Moreover, molecules docking process provided potential mechanism and Structure-Activity Relationships (SAR) of dual-target inhibition further.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3113-3130"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142998204","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":"Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers.","authors":"Manisha Yadav, Baddipadige Raju, Gera Narendra, Jasveer Kaur, Manoj Kumar, Om Silakari, Bharti Sapra","doi":"10.1007/s11030-024-11062-w","DOIUrl":"10.1007/s11030-024-11062-w","url":null,"abstract":"<p><p>The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual screening (VS) to identify hydrophobic drugs that exhibited better skin permeability in the presence of permeation enhancers i.e. menthol and limonene. The drugs identified through ML-based VS underwent experimental validation using in vitro skin penetration studies. The developed model predicted 80% probability of permeability enhancement for Sumatriptan Succinate (SS), Voriconazole (VCZ), and Pantoprazole Sodium (PS) with menthol and limonene. The in vitro release studies revealed that menthol increased penetration by approximately 2.49-fold, 2.25-fold, and 4.96-fold for SS, VCZ, and PS, respectively, while limonene enhanced permeability by approximately 1.32-fold, 2.27-fold, and 3.7-fold for SS, VCZ, and PS. The results from in silico and in vitro studies were positively correlated, indicating that the developed ML models could effectively reduce the need for extensive in vitro and in vivo experimentation.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3131-3146"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827139","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":"\"Several birds with one stone\": exploring the potential of AI methods for multi-target drug design.","authors":"Muhetaer Mukaidaisi, Madiha Ahmed, Karl Grantham, Aws Al-Jumaily, Shoukat Dedhar, Michael Organ, Alain Tchagang, Jinqiang Hou, Syed Ejaz Ahmed, Renata Dividino, Yifeng Li","doi":"10.1007/s11030-024-11042-0","DOIUrl":"10.1007/s11030-024-11042-0","url":null,"abstract":"<p><p>Drug discovery is a time-consuming and expensive process. Artificial intelligence (AI) methodologies have been adopted to cut costs and speed up the drug development process, serving as promising in silico approaches to efficiently design novel drug candidates targeting various health conditions. Most existing AI-driven drug discovery studies follow a single-target approach which focuses on identifying compounds that bind a target (i.e., one-drug-one-target approach). Polypharmacology is a relatively new concept that takes a systematic approach to search for a compound (or a combination of compounds) that can bind two or more carefully selected protein biomarkers simultaneously to synergistically treat the disease. Recent studies have demonstrated that multi-target drugs offer superior therapeutic potentials compared to single-target drugs. However, it is intuitively thought that searching for multi-target drugs is more challenging than finding single-target drugs. At present, it is unclear how AI approaches perform in designing multi-target drugs. In this paper, we comprehensively investigated the performance of multi-objective AI approaches for multi-target drug design. Our findings are quite counter-intuitive demonstrating that, in fact, AI approaches for multi-target drug design are able to efficiently generate more high-quality novel compounds than the single-target approaches while satisfying a number of constraints.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3023-3039"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142708671","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":"Probing the dark chemical matter against PDE4 for the management of psoriasis using in silico, in vitro and in vivo approach.","authors":"B Swapna, Satvik Kotha, Divakar Selvaraj, Siddamsetty Ramachandra, Aruna Acharya","doi":"10.1007/s11030-025-11159-w","DOIUrl":"10.1007/s11030-025-11159-w","url":null,"abstract":"<p><p>The potential downsides for the present treatment for psoriasis are drug resistance, reduced efficacy, risk of mental episodes, and drug interactions. Hence, this study aims to discover a new drug for psoriasis by considering global research efforts and exploring underrepresented chemical space regions. The objective was to identify novel PDE4D inhibitors from the dark chemical matter (DCM) database for treating psoriasis. To address this we have coupled molecular docking and pharmacophore screening with molecular dynamics (MD) to identify hit molecules. Additionally, pharmacokinetics optimization was performed using machine learning and artificial intelligence which are key parts of drug discovery and development processes. The 139,353 DCM molecules were evaluated for their binding mode and interaction with critical residues such as GLN369, ILE336, PHE340, and PHE372 of the phosphodiesterase-4D (PDE4D) enzyme. Here, 15 hits were obtained through successive virtual screening procedures and all the 15 molecules were subjected to MD simulations for hit identification. In the MD studies, a stable root mean square deviation (RMSD) and ligand-protein interactions were found with four molecules, namely 027230, 060628, 060576, and 085881. The ligand 085881 was found promising because it inhibits LPS-induced IL-6 and TNF-alpha secretion from THP-1 cells with IC₅₀ of 18.41 μM and 34.43 μM, respectively. In vivo erythema grading showed that 085881 possesses mild to moderate anti-psoriatic action. This study demonstrates the effective use of computational techniques to discover novel PDE4D inhibitors and provides insight into their therapeutic potential for treating inflammatory diseases such as psoriasis.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3449-3464"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143646905","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 novel potential 11β-HSD1 inhibitors through combining deep learning, molecular modeling, and bio-evaluation.","authors":"Xiaodie Chen, Liang Zou, Lu Zhang, Jiali Li, Rong Liu, Yueyue He, Mao Shu, Kuilong Huang","doi":"10.1007/s11030-025-11171-0","DOIUrl":"10.1007/s11030-025-11171-0","url":null,"abstract":"<p><p>11β-Hydroxysteroid dehydrogenase type 1 (11β-HSD1) has been shown to play an important role in the treatment of impaired glucose tolerance, insulin resistance, dyslipidemia, and obesity and is a promising drug target. In this study, we built a gated recurrent unit (GRU)-based recurrent neural network using 1,854,484 (processed) drug-like molecules from ChEMBL and the US patent database and successfully built a molecular generative model of 11βHSD1 inhibitors by using the known 11β-HSD1 inhibitors that have undergone transfer learning, our constructed GRU model was able to accurately capture drug-like molecules evaluated using traditional machine model-related syntax, and transfer learning can also easily generate potential 11β-HSD1 inhibitors. By combining Lipinski's and absorption, distribution, metabolism, excretion, and toxicity (ADME/T) analyses to filter nonconforming molecules and stepwise screening through molecular docking and molecular dynamics simulation, we finally obtained 5 potential compounds. We found that compound 02 is identical to a previously published inhibitor of 11β-HSD1. We selected compounds 02 and 05 with the lowest binding free energy for in vitro activity validation and found that compound 02 possessed inhibitory activity but was not as potent as the control. In conclusion, our study provides new ideas and methods for the development of new drugs and the discovery of new 11β-HSD1 inhibitors.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3485-3500"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144109463","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":"Machine learning: Python tools for studying biomolecules and drug design.","authors":"Fedor V Ryzhkov, Yuliya E Ryzhkova, Michail N Elinson","doi":"10.1007/s11030-025-11199-2","DOIUrl":"10.1007/s11030-025-11199-2","url":null,"abstract":"<p><p>The increasing adoption of computational methods and artificial intelligence in scientific research has led to a growing interest in versatile tools like Python. In the fields of medical chemistry, biochemistry, and bioinformatics, Python has emerged as a key language for tackling complex challenges. It is used to solve various tasks, such as drug discovery, high-throughput and virtual screening, protein and genome analysis, and predicting drug efficacy. This review presents a list of tools for these tasks, including scripts, libraries, and ready-made programs, and serves as a starting point for scientists wishing to apply automation or optimization to routine tasks in medical chemistry and bioinformatics.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3789-3824"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143956964","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":"Deep learning in the discovery of antiviral peptides and peptidomimetics: databases and prediction tools.","authors":"Maryam Nawaz, Yao Huiyuan, Fahad Akhtar, Ma Tianyue, Heng Zheng","doi":"10.1007/s11030-025-11173-y","DOIUrl":"10.1007/s11030-025-11173-y","url":null,"abstract":"<p><p>Antiviral peptides (AVPs) represent a novel and promising therapeutic alternative to conventional antiviral treatments, due to their broad-spectrum activity, high specificity, and low toxicity. The emergence of zoonotic viruses such as Zika, Ebola, and SARS-CoV-2 have accelerated AVP research, driven by advancements in data availability and artificial intelligence (AI). This review focuses on the development of AVP databases, their physicochemical properties, and predictive tools utilizing machine learning for AVP discovery. Machine learning plays a pivotal role in advancing and developing antiviral peptides and peptidomimetics, particularly through the development of specialized databases such as DRAVP, AVPdb, and DBAASP. These resources facilitate AVP characterization but face limitations, including small datasets, incomplete annotations, and inadequate integration with multi-omics data.The antiviral efficacy of AVPs is closely linked to their physicochemical properties, such as hydrophobicity and amphipathic α-helical structures, which enable viral membrane disruption and specific target interactions. Computational prediction tools employing machine learning and deep learning have significantly advanced AVP discovery. However, challenges like overfitting, limited experimental validation, and a lack of mechanistic insights hinder clinical translation.Future advancements should focus on improved validation frameworks, integration of in vivo data, and the development of interpretable models to elucidate AVP mechanisms. Expanding predictive models to address multi-target interactions and incorporating complex biological environments will be crucial for translating AVPs into effective clinical therapies.</p>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":" ","pages":"3753-3788"},"PeriodicalIF":3.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735606","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}