Current computer-aided drug design最新文献

{"title":"Computational Identification of Potential HMG-CoA Reductase Modulators Through Drug Repurposing: Structural Insights and Therapeutic Implications.","authors":"Qusay Abdulsattar Mohammed, Mohammed R Al-Shaheen","doi":"10.2174/0115734099490379260809123720","DOIUrl":"https://doi.org/10.2174/0115734099490379260809123720","url":null,"abstract":"<p><strong>Introduction: </strong>HMG-CoA reductase (HMGCR) is a validated lipid-lowering target, but statin intolerance, variable response, and drug-drug interactions justify exploration of mechanistically distinct modulators. This study prioritized approved drugs that may bind predicted non-orthosteric HMGCR pockets.</p><p><strong>Methods: </strong>A computational workflow combining homology modeling (SWISS-MODEL; GMQE = 0.95), CB-Dock pocket detection, AutoDock Vina docking, 100 ns all-atom molecular dynamics (MD) simulations (GROMACS 2023.2), MM/PBSA binding-free-energy estimation, and in silico ADMET screening was applied to Fluspirilene, Lenvatinib, Siponimod, and Lumacaftor.</p><p><strong>Results: </strong>Two putative non-orthosteric pockets were identified with calculated volumes of 394 Å3 (Pocket 1) and 305 Å3 (Pocket 2). Fluspirilene showed the most favorable Pocket 1 docking score (-9.4 kcal/mol) and MM/PBSA binding-free-energy estimate (-65.2 ± 3.8 kcal/mol), whereas Lumacaftor showed the most favorable Pocket 2 score (-8.9 kcal/mol; MM/PBSA: -55.7 ± 4.1 kcal/mol). MD trajectories supported retention of the selected poses throughout the 100 ns simulation window.</p><p><strong>Discussion: </strong>The predicted interactions involved hydrophobic contacts, π-associated interactions, hydrogen bonding, and electrostatic contacts at sites spatially distinct from the canonical statin/HMG-CoA catalytic region. These findings support a non-orthosteric binding hypothesis but do not establish functional allosteric inhibition without enzyme kinetics, direct binding assays, and structural validation.</p><p><strong>Conclusion: </strong>The study identifies computationally prioritized candidate scaffolds for putative non-orthosteric HMGCR modulation and defines the biochemical, kinetic, and structural validation required before any therapeutic interpretation.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Mechanisms of Yifei Capsule in Stable COPD Treatment: An Integrated In Silico and Single-cell Transcriptomic Study.","authors":"Zongyu Li, Qi Lin, Hua Ye","doi":"10.2174/0115734099480941260729064043","DOIUrl":"https://doi.org/10.2174/0115734099480941260729064043","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Yifei Capsule (YFC), a Traditional Chinese Medicine (TCM) formulation, has shown clinical benefit in patients with stable Chronic Obstructive Pulmonary Disease (COPD). However, its molecular mechanisms remain insufficiently understood. This study aimed to systematically investigate the active compounds, key targets, and signaling pathways underlying the therapeutic effects of YFC in stable COPD by integrating computational pharmacology with single-cell transcriptomic validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;An integrated workflow combining network pharmacology, molecular docking, and 100 ns Molecular Dynamics (MD) simulations was used to identify bioactive compounds, candidate therapeutic targets, and major signaling pathways of YFC. To externally validate the in silico findings, an independent public single-cell RNA sequencing (scRNA-seq) dataset (GSE249584) containing 54,821 cells from 15 human samples (7 controls and 8 COPD patients) was analyzed to characterize the cell-type-specific expression patterns of hub genes in COPD.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 167 bioactive compounds in YFC and 977 putative targets were identified, of which 222 overlapped with COPD-related targets. Network topology analysis identified SRC, PIK3R1, and STAT3 as major hub genes, and KEGG enrichment analysis indicated that these targets were primarily enriched in the PI3K-Akt signaling pathway (hsa04151, p = 1.23E-15). Molecular docking revealed strong binding affinities between representative compounds and core targets, while MD simulations confirmed the dynamic stability of key complexes, particularly SRC-Korseveriline. scRNA-seq analysis further demonstrated cell-type-specific dysregulation of hub genes in COPD: STAT3 was significantly upregulated in monocyte/macrophage populations (adjusted p = 6.59E-08, log2FC = 0.32), PIK3R1 was downregulated in T cells (adjusted p = 2.05E-20, log2FC = -0.41), and SRC showed bimodal expression, with upregulation in endothelial cells (adjusted p = 0.002, log2FC = 0.38) but downregulation in T cells (adjusted p = 0.03, log2FC = -0.27).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;These findings suggest that YFC may exert therapeutic effects in stable COPD through multi-component, multi-target regulation of the PI3K-Akt pathway and related inflammatory signaling networks. The cell-type-specific dysregulation of SRC, PIK3R1, and STAT3 supports a nuanced immunomodulatory and vascular-regulatory mechanism, highlighting the value of integrating computational prediction with single-cell transcriptomic evidence.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This multi-scale study provides a mechanistic framework for understanding the action of YFC in stable COPD and identifies SRC, PIK3R1, and STAT3 as plausible core targets. The results support the utility of combining network pharmacology, molecular simulation, and single-cell transcriptomics to dissect the mechanisms of complex herbal medicines. Neverthele","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fuzzy Support Vector Regression for QSAR Modeling of 9,10-Dihydrophenanthrene Derivatives Against SARS-CoV-2 3CLpro.","authors":"Sopon Wiriyarattanakul, Phornphimon Maitarad, Rongrong Jia, Thanyada Rungrotmongkol, Amphawan Wiriyarattanakul","doi":"10.2174/0115734099495341260731052218","DOIUrl":"https://doi.org/10.2174/0115734099495341260731052218","url":null,"abstract":"<p><strong>Introduction/objective: </strong>Quantitative Structure-Activity Relationship (QSAR) modeling is a critical computational strategy in drug discovery; however, data heterogeneity and experimental noise frequently compromise model reliability. In this study, QSAR models were developed to predict the inhibitory activity of 9,10-dihydrophenanthrene derivatives against the SARS-CoV-2 3CLpro enzyme using a novel fuzzy Support Vector Regression (FSVR) framework. The proposed approach incorporates a fuzzy membership function to weight activity data according to their reliability and employs both the full molecular descriptor set and a subset refined by Variance Inflation Factor (VIF) analysis.</p><p><strong>Methods: </strong>Four regression models-FSVR-VIF, FSVR-All, SVR-VIF, and SVR-All-were constructed using a radial basis function kernel, with hyperparameters optimized via grid search combined with 10-fold cross-validation. Model performance was evaluated using the coefficient of determination (R²) and the root-mean-square error (RMSE).</p><p><strong>Results: </strong>FSVR consistently outperformed conventional SVR in terms of accuracy and robustness. Among the four models, FSVR-VIF achieved the best cross-validation performance (R²(CV) = 0.9621). The model also demonstrated good predictive performance on the independent external validation set, yielding an R²(ext) of 0.828. Residual error analyses revealed symmetrically distributed residuals with markedly fewer extreme deviations for the fuzzy-enhanced models. Notably, the FSVR-VIF model exhibited a narrow residual spread, with most compounds falling within ±0.5 units across both the training and test sets.</p><p><strong>Discussion: </strong>These findings suggest that the incorporation of fuzzy logic improved model robustness against data uncertainty, while VIF-based descriptor selection enhanced model parsimony and interpretability. Furthermore, descriptor analysis indicated that molecular symmetry and mass distribution (e.g., X23) were positively associated with inhibitory activity, whereas increased methyl substitution and structural branching characteristics (e.g., X7 and X11) tended to be associated with reduced inhibitory potency.</p><p><strong>Conclusion: </strong>Overall, the integration of fuzzy logic with SVR and VIF-based feature selection provided a parsimonious local QSAR model for the investigated series of 9,10-dihydrophenanthrene derivatives, yielding improved predictive performance and descriptor interpretability while maintaining satisfactory predictive capability on an independent external dataset.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Docking and Pharmacological In Silico Evaluation of Genistein and Hematoxylin as Promising Ribonucleotide Reductase-targeted Therapies for Cancer.","authors":"Oun Deli Khudhair, Muhammad Azrul Zabidi, Amirah Mohd Gazzali, Mohd Nadzri Mohd Najib, Taif M Maryoosh","doi":"10.2174/0115734099493491260722061345","DOIUrl":"https://doi.org/10.2174/0115734099493491260722061345","url":null,"abstract":"<p><strong>Background: </strong>Ribonucleotide Reductase (RR) is a pivotal enzyme in DNA synthesis and repair, making it a vital target in cancer therapy. While synthetic RR inhibitors such as gemcitabine and hydroxyurea are used clinically, their long-term efficacy is hampered by drug resistance and systemic toxicity. This has prompted growing interest in natural compounds with potential anticancer properties and reduced toxicity. This study aims to evaluate the molecular interactions and pharmacokinetic profiles of two naturally derived compounds traditionally used, genistein (Chinese name: , derived from Glycine max) and hematoxylin (Chinese name: , extracted from Caesalpinia sappan), against human RR using molecular docking and in silico pharmacological analyses.</p><p><strong>Methods: </strong>The three-dimensional structure of human RR (PDB ID: 6L7L) was retrieved and subjected to docking simulations using AutoDock Vina and Chimera. Genistein and hematoxylin were docked into the RR active site, and their interactions were analyzed in comparison to gemcitabine. Molecular dynamics simulations over 100 ns further confirmed the structural stability and binding persistence of both compounds within the RR active site. Pharmacokinetic and drug-likeness properties were evaluated via SwissADME and Molinspiration platforms.</p><p><strong>Results: </strong>Both genistein and hematoxylin exhibited strong binding affinities (-8.2 kcal/mol, -8.8 kcal/mol), respectively, to the RR active site, interacting with key catalytic residues in a manner comparable to the standard drug gemcitabine (-7.3 kcal/mol). Molecular dynamics simulations over 100 ns further confirmed the structural stability and binding persistence of both compounds within the RR active site. In silico pharmacokinetic predictions indicated good oral bioavailability, compliance with drug-likeness criteria, and low predicted toxicity.</p><p><strong>Discussion: </strong>Genistein emerges as a good candidate owing to its strong RRM1 binding, favorable physicochemical attributes, clean medicinal chemistry profile, and low predicted toxicity. Hematoxylin offers superior binding affinity but requires refinement to address solubility and PAINS-related concerns. Gemcitabine, while efficacious clinically, suffers from limited oral bioavailability and potential toxicities.</p><p><strong>Conclusion: </strong>Genistein and hematoxylin demonstrate promising molecular interactions and pharmacological profiles as potential natural RR inhibitors. Their dual role in traditional healing and modern molecular pharmacology supports further preclinical development as anticancer agents.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148708759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating the Activity of E-Lian Granule in Alleviating Chronic Atrophic Gastritis: Insights from Network Pharmacology, Molecular Docking, and Experimental Validation.","authors":"Yini Tang, Yan Xue, Shigui Xue, Shuang Gao, Gan Cai, Danbo Dou, Mingrong Yao, Sizhen Gu","doi":"10.2174/0115734099515940260708054209","DOIUrl":"https://doi.org/10.2174/0115734099515940260708054209","url":null,"abstract":"<p><strong>Introduction: </strong>Chronic Atrophic Gastritis (CAG) and Intestinal Metaplasia (IM) are critical precancerous lesions of gastric cancer. E-Lian granule (ELKL), a traditional Chinese medicine formula, has demonstrated favorable clinical efficacy in treating precancerous gastric lesions; however, its underlying molecular mechanisms remain unclear.</p><p><strong>Methods: </strong>A comprehensive approach integrating network pharmacology, molecular docking, and experimental validation was employed to investigate the pharmacological mechanisms of ELKL against CAG and IM. Potential active compounds, therapeutic targets, and signaling pathways were identified through database analysis, Protein-Protein Interaction (PPI) networks, and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Molecular docking was utilized to evaluate compound-target interactions. In vitro experiments were performed using MNNG-induced human Gastric Epithelial Cells (GES-1) treated with magnoflorine, columbamine, or 3-O-acetyl-glycyrrhetinic acid, and in vivo validation was conducted in a CAG mouse model.</p><p><strong>Results: </strong>A total of 178 overlapping therapeutic targets were identified. KEGG analysis revealed that the TNF, MAPK, and PI3K/Akt signaling pathways were closely associated with the therapeutic effects of ELKL. AKT1, MAPK1, and NFKB1 were identified as key hub targets. Molecular docking demonstrated strong and stable binding affinities of magnoflorine, columbamine, and 3-O-acetyl-glycyrrhetinic acid toward these targets. In vitro and in vivo assays comprehensively confirmed that these compounds significantly attenuated inflammatory cytokine secretion and inhibited the expression of AKT1, MAPK1, and NFKB1.</p><p><strong>Discussion: </strong>These findings indicate that ELKL exerts significant anti-inflammatory effects through the multi-component and multi-target regulation of inflammatory signaling pathways, thereby supporting its therapeutic potential in preventing the progression of CAG and IM.</p><p><strong>Conclusion: </strong>The representative active compounds identified from ELKL may ameliorate CAG and IM by regulating the TNF, MAPK, and PI3K/Akt signaling pathways.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148709038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Elucidating the Potential Therapeutic Role of Gut Microbiota Metabolites in Atrial Fibrillation Through Network Pharmacology Combined with Target Omics.","authors":"Yi Ning, Yanglulu Su, Guoqiang Zhong","doi":"10.2174/0115734099488107260715104643","DOIUrl":"https://doi.org/10.2174/0115734099488107260715104643","url":null,"abstract":"<p><strong>Introduction: </strong>Atrial Fibrillation (AF) is a prevalent arrhythmia with complex pathogenesis and limited optimal treatments. This study aims to explore the possible protective effects of gut microbiota metabolites against AF and the underlying mechanisms.</p><p><strong>Methods: </strong>This study integrated network pharmacology, molecular docking, and molecular dynamics simulations to explore gut microbiota metabolites' role in AF. Metabolite targets were retrieved from gutMGene, Swiss Target Prediction, and SEA; AF-related genes from Gene- Cards, OMIM, and CTD. SwissADME and ADMETlab screened metabolites for drug-likeness and toxicity, while molecular docking and 100 ns molecular dynamics simulations verified the binding stability between metabolites and core target binding stability.</p><p><strong>Results: </strong>A total of 22 intersecting targets were identified; protein-protein interaction analysis showed AKT1, IL6, and JUN as core hub genes. GO/KEGG enrichment indicated enrichment in oxidative stress, miRNA transcriptional regulation, PI3K-Akt, and MAPK pathways. Six safe metabolites (e.g., linoleic acid, hesperetin dihydrochalcone) were screened. Molecular docking confirmed their stable binding to core targets (naringenin chalcone-JUN and 5-(3,4- dihydroxyphenyl)-4-hydroxypentanoic acid-JUN had the strongest affinity), validated by 100 ns molecular dynamics simulations.</p><p><strong>Conclusion: </strong>Gut microbiota metabolites may regulate AF-related signaling pathways via AKT1, IL6, and JUN, supporting the gut-heart axis as an AF intervention framework. The six metabolites are promising lead molecules for gut-targeted AF therapy, pending further experimental validation.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148611028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Pharmacology-guided Target Evaluation of Nefopam for Alzheimer's Disease: Insights from Docking and Molecular Dynamics Simulations.","authors":"Mayank Saini, Tanuj Hooda, Mohammad Ovais Dar, Sandeep Kumar, Ramchander Khatri, Amit Lather","doi":"10.2174/0115734099474093260712091739","DOIUrl":"https://doi.org/10.2174/0115734099474093260712091739","url":null,"abstract":"<p><strong>Introduction: </strong>Amyloid-β accumulation, aberrant tau protein, neuroinflammation, and oxidative stress are some of the main pathogenic characteristics of Alzheimer's disease (AD), a degenerative illness characterized by cognitive deterioration. The majority of current AD therapies provide symptomatic alleviation with significant adverse effects, highlighting the urgent need for novel therapeutic approaches.</p><p><strong>Objective: </strong>This study examines nefopam, a centrally acting analgesic with NMDA antagonist and monoaminergic properties, as a potential treatment for AD using in silico methods like Network Pharmacology, Docking, and Molecular Dynamics Simulation studies.</p><p><strong>Results: </strong>Using network pharmacology, 90 molecular targets shared by the AD and nefopam pathways were identified. Following the selection of important hub proteins for further analysis, eight proteins with accessible 3D structures were put through molecular docking and MMGBSA computations. Nefopam demonstrated significant binding affinities, especially to 5HTR2A, GRIN1, 5HTR2C, SLC6A4, SLC6A3, and MAOB, whereas OPRM1 displayed weaker interactions, consistent with its lower MM-GBSA value (-37.04 kcal/mol) and docking score (-2.963). Molecular dynamics simulations of particular complexes over 100 ns revealed stable contacts and minimal structural changes for SLC6A3, SLC6A4, and 5HTR2A, suggesting strong and long-lasting binding.</p><p><strong>Discussion: </strong>The findings suggest that nefopam exhibits significant multi-target interactions with several proteins involved in AD pathogenesis, particularly those associated with neurotransmission, neuroprotection, and neuroinflammatory pathways. Its stable binding behavior and favorable interaction profiles support its potential role in modulating disease progression beyond symptomatic management.</p><p><strong>Conclusion: </strong>Overall, this computational analysis confirms that nefopam can target multiple proteins linked to AD. These results show that more experimental research is necessary to validate nefopam's therapeutic potential and provide positive support for its repositioning in AD treatment.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148611052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Omeprazole Effect on Pro-apoptotic Factors in Colorectal Cancer Treatment: Insights from In Silico Studies and Antiproliferative Assessment on HT29 Cells.","authors":"Tooba Abdizadeh, Sana Banisakin, Keyhan Ghatreh Samani, Najmeh Salehi-Vanani, Javad Saffari-Chaleshtori","doi":"10.2174/0115734099434355260610050251","DOIUrl":"https://doi.org/10.2174/0115734099434355260610050251","url":null,"abstract":"<p><strong>Background: </strong>Colorectal Cancer (CRC) is one of the most common cancers worldwide. There is an urgent need to identify new drugs for CRC treatment. The apoptotic factors Bad, Bid, Bik, Bim, Bax, and Bak play a crucial role in activating the apoptotic cascade pathways. Inhibition of these factors is a main strategy of cancer therapy. This study investigates the effects of omeprazole on pro-apoptotic factors (Bak, Bad, Bim, Bik, Bid, and Bax) involved in the apoptosis induction in CRC using both in-silico and in-vitro techniques.</p><p><strong>Methods: </strong>The 3D structure of omeprazole was docked to pro-apoptotic factors obtained from the Protein Data Bank (PDB) using Autodock 4.2.6 software. Molecular Dynamics (MD) simulations were also carried out with GROMACS 2022 to validate the stability of the ligandreceptor complexes. Additionally, an in-vitro experiment was performed to evaluate the cytotoxicity of omeprazole on the HT-29 cell line using the MTT assay.</p><p><strong>Results: </strong>The strong binding affinity between omeprazole and pro-apoptotic factors was observed in molecular docking, with binding energies less than -5.0 kcal/mol. The stability of the omeprazole-receptor complexes was confirmed through MD simulations. Moreover, in vitro results indicated that omeprazole can inhibit the growth and proliferation of colorectal cancer cells with an IC50 value of 400 μM.</p><p><strong>Discussion: </strong>In silico studies presented that omeprazole could affect pro-apoptotic factors responsible for inducing apoptotic pathways. The in vitro studies assume that omeprazole has significant cytotoxic activity against colorectal cancer cell lines.</p><p><strong>Conclusion: </strong>These results demonstrated the therapeutic effects of omeprazole by inducing apoptosis in colorectal cancer through the influence of pro-apoptotic factors, and it appears that omeprazole could be a promising drug repurposing candidate for the treatment of colorectal cancer.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148551652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Network Pharmacology Revealed by High-throughput Blind Docking: Deciphering the Multitarget Synergy of Taohe Chengqi Decoction against Constipation.","authors":"Xiaoli Chang, Linbing Yu, Xudong Wu, Daijing Wei, Yuan Gao","doi":"10.2174/0115734099418044251129105719","DOIUrl":"https://doi.org/10.2174/0115734099418044251129105719","url":null,"abstract":"<p><strong>Introduction: </strong>Constipation, prevalent globally (16-35%), is particularly common in the elderly, reducing quality of life and causing complications like intestinal obstruction. Current treatments (e.g., methylcellulose, polyethylene glycol, bisacodyl, prucalopride) provide temporary relief but have side effects, such as electrolyte disturbances and drug dependence. It is worth noting that the traditional Chinese herbal drug Taohe Chengqi Decoction (TCD) has demonstrated good laxative efficacy and safety advantages in long-term clinical practice. However, its systematic mechanism of action involving multiple components, multiple targets, and multiple pathways remains unclear.</p><p><strong>Methods: </strong>This study innovatively integrates network pharmacology prediction, high-throughput blind molecular docking assisted by machine learning (DiffDock), and molecular dynamics simulation to construct a multidimensional mechanism model for TCD in treating constipation.</p><p><strong>Results: </strong>A total of 115 active molecules were identified from TCD through high-throughput blind docking and network pharmacology, targeting 153 constipation-related proteins. After an 80 ns molecular dynamics simulation, six core components demonstrated stable binding with key targets like AKT1 and MAPK3.</p><p><strong>Discussions: </strong>TCD exerts its therapeutic effects by intervening in key signaling pathways such as PI3K-Akt, IL-17 inflammation, T cell receptor, MAPK, and cAMP. It regulates the dynamic balance of intestinal epithelial cell proliferation/apoptosis, inhibits inflammatory cascades, and enhances intestinal motility and secretory functions to relieve constipation.</p><p><strong>Conclusion: </strong>The innovative integration of machine learning-driven high-throughput blind docking technology with network pharmacology establishes a novel paradigm for traditional Chinese medicine research, significantly enhancing target prediction reliability through advanced structural validation.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Novel PKC Alpha Inhibitors: Structural and Functional Characterization Using In Silico and In Vitro Studies.","authors":"Shiven Das, Devika S Kumar, Arunasree Kalle","doi":"10.2174/0115734099471633260629213423","DOIUrl":"https://doi.org/10.2174/0115734099471633260629213423","url":null,"abstract":"<p><strong>Introduction: </strong>Protein Kinase C alpha (PKCα), a key regulator of cellular signaling, is frequently dysregulated in breast cancer. Despite its clinical relevance, there is a dearth of selective and safe PKCα inhibitors. This study aims to identify and characterize novel PKCα inhibitors using computational and experimental methodologies.</p><p><strong>Methods: </strong>A structure-based virtual screening of the Enamine Hinge Binders Library against the PKCα receptor (PDB ID: 3IW4) was performed using the Glide module of Schrödinger Suite. Analysis of docking scores, protein-ligand interactions, binding free energies, and ADMET profiles helped identify lead candidates. The stability of protein-ligand complexes was confirmed using MD simulations. The functional activity of identified candidates was validated using a kinase assay, and cell death was analyzed using an MTT assay on breast cancer cell lines.</p><p><strong>Results: </strong>Compounds Z3077775938 (P1) and Z22177390 (P2) exhibited strong binding and stability within the hinge-binding region of PKCα and showed favorable drug-likeness profiles. Structural analysis revealed P1 and P2 as novel scaffolds. The kinase assay revealed dosedependent inhibition of PKCα's ATP-binding activity with IC50 values of P1 and P2 as 0.92 nM and 15.27 nM, respectively. Both compounds induced dose-dependent cytotoxicity in breast cancer cells.</p><p><strong>Discussion: </strong>This study identified two novel, structurally diverse PKCα inhibitors. Their low IC50 values and dose-dependent cytotoxicity, as observed in both ER+ and triple-negative breast cancer cell lines, validate their functional efficacy and highlight therapeutic potential for breast cancer treatment.</p><p><strong>Conclusion: </strong>Overall, this study identified two PKCα inhibitors that provide a foundation for further optimization as selective therapeutic agents in PKCα-driven malignancies.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}