Current computer-aided drug design最新文献

{"title":"Harnessing the Power of Large Language Models for Drug Discovery: A Systematic Review of Current Applications and Future Directions.","authors":"Pranay Wal, Manmohan Shukla, Kritika Sachan, Ankita Wal, Hanish Singh Jayasingh Chellammal, Mohit Tiwari, Amin Gasmi","doi":"10.2174/0115734099432517260829135754","DOIUrl":"https://doi.org/10.2174/0115734099432517260829135754","url":null,"abstract":"<p><strong>Introduction: </strong>The demand for inventive approaches to drug discovery has increased due to the rising costs, time, and failure rates in pharmaceutical research. Large Language Models (LLMs), with their sophisticated natural language processing and generative capabilities, have become potent instruments that have the potential to revolutionize biomedical research. The function of LLMs in different phases of drug development is methodically examined in this article.</p><p><strong>Methods: </strong>The PRISMA 2020 principles were adhered to in this systematic study. A thorough search for research published between 2018 and 2025 was done using PubMed, Scopus, Web of Science, and Google Scholar. The search terms \"large language model,\" \"transformer,\" \"drug discovery,\" and important sub-domains (such as \"de-novo design\" and \"ADMET\") were merged, and two reviewers independently screened the results. Predetermined inclusion and exclusion criteria were used to filter studies for relevance. 98 studies out of the 1,285 records that were initially retrieved met the requirements for the final qualitative synthesis.</p><p><strong>Results: </strong>98 studies that demonstrated the use of LLMs in various drug discovery domains were found during the review. These covered molecular generation, genomics, protein-ligand modeling, ADME/T and toxicity profiling, drug-target interaction and DTI prediction, and biomedical text mining. 42 different LLM-based tools were mapped, including BioBERT, SciSpacy, Drug- LLM, DNA-BERT, GPT-4, and ChatGPT. Predictive accuracy, hypothesis creation, target prioritization, and multi-modal data integration all showed notable gains with these techniques.</p><p><strong>Discussion: </strong>By providing scalable, precise, and effective solutions for data-driven drug discovery, LLMs are revolutionizing the pharmaceutical industry. They allow for the creation of hypotheses and individualized insights across multi-modal biological data, and they perform better than conventional approaches in a number of subdomains. Improvements in performance were task-dependent; the most consistent gains occurred for biomedical text mining, disease-genedrug relationship mapping and drug-target interaction prediction tasks. Yet most evidence for clinical applications is still derived from retrospective studies and benchmark datasets, suggesting a higher need for prospective validation.</p><p><strong>Conclusion: </strong>There is revolutionary potential in incorporating LLMs into drug discovery processes. Clinical translation and regulatory uptake will depend heavily on collaborative validation, ethical deployment, and standardization as models become more multimodal and interpretable. Before normal use, extensive prospective benchmarking and head-to-head comparisons with established chemoinformatics pipelines are necessary.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972763","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":"In silico Molecular Docking Analysis of Bioactive Phytoconstituents against Environmental Neurotoxin Bisphenol-A.","authors":"Sagarika Majhi, Prashant Sharma, Himanshu Tyagi, Tanisha Chaudhary, Lubhan Singh, Ritu Singh","doi":"10.2174/0115734099493536260813101633","DOIUrl":"https://doi.org/10.2174/0115734099493536260813101633","url":null,"abstract":"<p><strong>Introduction: </strong>Cognitive impairment, also commonly referred to as intellectual disability, is a complex disorder that affects a person's ability to think, remember, and learn. Bisphenol A is an environmental toxicant that has detrimental effects on the nervous system's functioning over prolonged periods. Bioactive phytochemicals are a viable choice for promoting cognitive health in ageing populations, as they are widely available, reasonably priced, and generally have fewer adverse effects than synthetic medications.</p><p><strong>Methods: </strong>The in silico study was designed to predict the binding potential of bioactive phytoconstituents, viz. caffeic acid, chlorogenic acid, ferulic acid, and picroside II, against various selected targets. ΔG and ADMET predictions were carried out to determine physicochemical parameters and drug-likeness. 2D and 3D structural analysis, along with bioavailability radar and the boiled egg model, was conducted to study the bioavailability of selected ligands.</p><p><strong>Results: </strong>Molecular docking study conducted on eight different molecular targets with a G score of -5.5 to -10.3 kcal/mol, indicating the ligands with the highest predicted binding affinity (picroside II > chlorogenic acid > caffeic acid > ferulic acid). ΔG, ADMET showed BPA as a toxicant with the best molecular docking at COX-2, IL-2, IL-12, and LC3B targets.</p><p><strong>Discussion: </strong>As a protagonist against BPA, chlorogenic acid and picroside II showed similar molecular docking. The bioavailability radar and boiled egg model showed poor bioavailability of chlorogenic acid and picroside II as compared to caffeic acid and ferulic acid. Overall, the compounds represented a desirable ADMET profile (caffeic acid > ferulic acid > chlorogenic acid > picroside II).</p><p><strong>Conclusion: </strong>This study has enabled the development of future biocompatible and multi-target bioactive phytoconstituents for treating complex nervous system diseases like cognitive impairment, where targeting multiple pathways simultaneously can improve therapeutic outcomes.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972772","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":"Transcriptomic and Network Pharmacology Analysis of Targets Associated with Osteoporosis Improvement by Longzhong Sunshang Capsule and Experimental Validation.","authors":"Randong Peng, Hongxi Liu, Wenbo Yang, Jun Zhou, Yulei Wang, Yang Yang, Yandong Liu, Xuesong Yin, Junwei Bi","doi":"10.2174/0115734099484241260821114935","DOIUrl":"https://doi.org/10.2174/0115734099484241260821114935","url":null,"abstract":"<p><strong>Introduction: </strong>Osteoporosis (OP) severely impacts morbidity, mortality, and quality of life in affected individuals. This study aimed to screen key targets of Longzhong Sunshang Capsule for the treatment of OP via network pharmacology.</p><p><strong>Methods: </strong>Transcriptomic and target data were retrieved from public databases, followed by differential expression analysis, machine learning, expression validation, and functional analyses (immune microenvironment, enrichment, molecular docking, molecular dynamics, toxicology, ELISA).</p><p><strong>Results: </strong>This study identified FOXO3, XDH, and PLG as key OP targets (validated by ROC curves, AUC > 0.7), primarily enriched in chromatin remodeling and ribosome pathways. XDH interacted with the largest number of active components and showed strong binding affinity with luteolin (-10.0 kcal/mol). Key active components had liver damage toxicity scores of 0.62- 0.72. ELISA detection revealed that serum concentrations of FOXO3, XDH, and Plg were all significantly higher in osteoporosis patients than those in healthy controls (p < 0.0001).</p><p><strong>Discussion: </strong>This study identified FOXO3, XDH, and PLG as key OP targets, highly expressed and diagnostically valuable, correlated with immune molecules, and enriched in chromatin remodeling and ribosomes. XDH has high diagnostic potential. Limitations include lack of in vitro/in vivo validation and insufficient FOXO3 mechanism characterization.</p><p><strong>Conclusion: </strong>FOXO3, XDH, and PLG are promising diagnostic targets, revealing the molecular mechanism underlying the anti-OP efficacy of Longzhong Sunshang Capsule.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972698","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":"Computational Identification of Shikimic Acid Derivatives as Potential SARS-CoV-2 Inhibitors: An <i>In Silico</i> Approach.","authors":"Hardha Balachandran, Kaviarasan Lakshmanan, Dipen Purohit, Jacob Stanley Vineth Martin, Gowramma Byran, Kalirajan Rajagopal","doi":"10.2174/0115734099485678260825093451","DOIUrl":"https://doi.org/10.2174/0115734099485678260825093451","url":null,"abstract":"<p><strong>Introduction: </strong>The emergence of the COVID-19 virus has presented a serious threat to global health, with its high contagion and fatality rates. Despite considerable efforts, the search for effective antiviral drugs is still limited and requires the identification of new therapeutic leads using state-of-the-art computational methods.</p><p><strong>Methods: </strong>Here, structure-activity relationship (SAR) analysis was used to design shikimic acidbased compounds as anti-SARS-CoV-2 agents. The compounds were docked using the Schrödinger suite and Discovery Studio to assess their binding interactions with the SARS-CoV-2 main protease. ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties were also evaluated in silico. The lead compound was subjected to molecular dynamics (MD) simulation (100 ns) to explore the dynamics of the protein-ligand interaction.</p><p><strong>Results: </strong>The docking results from Schrodinger suite ranged from -4.8 to -6.8 kcal/mol, while T1 and T2 showed the most favourable CDOCKER interaction energies ranged from -7.7 to -8.8 kcal/mol. Key interactions involved critical amino acid residues such as SER46, MET49, HIE41, GLN189, ARG188, ASP187, MET165, HIE164, THR24, THR25, LEU27, ASN142, and GLY143. The docked pose of the co-crystal ligand confirmed the docking protocol (RMSD = 0.9875 Å). The Discovery Studio results were in good agreement with Schrodinger, and ADMET predictions suggested good drug-like properties. Moreover, the 100-ns MD simulation showed the T2-protein complex to be stable.</p><p><strong>Discussion: </strong>The docking, ADMET, and MD simulation studies indicate that the designed shikimic acid derivatives have good binding affinity and drug-like properties, and are stable in the active site of the SARS-CoV-2 main protease. The results suggest T2 as a potential lead compound.</p><p><strong>Conclusion: </strong>In conclusion, the study suggests that the shikimic acid-derived T2 could be a potential SARS-CoV-2 inhibitor with strong in silico evidence. But additional in vitro and in vivo experiments are needed to confirm its efficacy.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148972749","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":"Computational Discovery of Phytochemicals Targeting InhA of Mycobacterium tuberculosis: Insights from Molecular Docking, MMGBSA, ADME Profiling, and Molecular Dynamics Simulations.","authors":"Dev Kumar, Bhoomika, Alka Khichi, Ritu Jakhar, Mehak Dangi","doi":"10.2174/0115734099478541260828062328","DOIUrl":"https://doi.org/10.2174/0115734099478541260828062328","url":null,"abstract":"<p><strong>Introduction: </strong>Tuberculosis (TB) is one of the most serious global health issues, with the increasing number of multidrug-resistant TB cases emphasizing the need for new therapeutic approaches. Phytochemicals, with their diverse structures and favorable safety profiles, are a largely unexplored area for anti-TB drug development.</p><p><strong>Methods: </strong>An ethnobotanical study and literature analysis identified 310 medicinal plants traditionally used to treat respiratory infections, which produced 4,087 phytochemicals. Their structures were obtained from PubChem or drawn using ChemDraw, and pharmacokinetic properties were analyzed using QikProp. Enoyl-acyl carrier protein reductase (InhA, PDB ID: 4TRO), an important enzyme involved in mycolic acid biosynthesis, was selected as the target protein. Molecular docking was performed using Glide, followed by MMGBSA calculations, and the best hits were validated by 300 ns molecular dynamics simulations using the GROMACS pipeline.</p><p><strong>Results: </strong>Binding affinities showed that four phytochemicals, namely Patuletin, skimmin, flavonol- 3-O-D-glycoside, and salicin, had significantly higher binding affinity scores (-10.06 to -8.82 kcal/mol) than first-line anti-TB drugs isoniazid (-6.35 kcal/mol) and pyrazinamide (-4.22 kcal/mol). Patuletin and skimmin had MM-GBSA binding affinity scores of -74.89 and -71.76 kcal/mol, respectively. MD simulations of the top 3 compounds and control showed that the protein-ligand complexes were stable, as indicated by the RMSD, RMSF, Radius of gyration, SASA, and HBONDS.</p><p><strong>Discussion: </strong>Patuletin and Skimmin demonstrated strong binding affinity and structural stability against the InhA enzyme (PDB: 4TRO), indicating their potential as promising lead compounds for anti-tuberculosis effects. Derived from ethnomedicinal plants, these phytochemicals not only target key mycobacterial pathways but may also offer hepatoprotective and immunomodulatory benefits. The findings support the exploration of plant-derived compounds as safer and effective alternatives or adjuncts to conventional anti-TB therapies.</p><p><strong>Conclusion: </strong>The combination of virtual screening, ADME studies, and MD simulations enabled the identification of phytochemicals with promising interactions toward InhA, an established anti-TB target. The findings are based solely on computational analysis and should be interpreted as preliminary evidence of target engagement rather than confirmed inhibitory activity or therapeutic efficacy. These natural products may serve as potential lead compounds for further anti-tubercular drug discovery, warranting subsequent biochemical, cellular, and in vivo validation to establish their inhibitory potential, safety, and pharmacological effectiveness.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148908316","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 and Experimental Validation of a Securidaca inappendiculata Xanthone Fraction Targeting Rheumatoid Arthritis via the PI3K/AKT/mTOR/HIF-1α/GSK-3β Axis.","authors":"Xuan Chen, Bingru Fan, Ting Wang, Yuyun Zhou, Wenli Ding, Yike Han, Guodong Wang, Yuyan Zhou","doi":"10.2174/0115734099490013260821075759","DOIUrl":"https://doi.org/10.2174/0115734099490013260821075759","url":null,"abstract":"<p><strong>Background: </strong>Securidaca inappendiculata is a traditional medicinal plant used for the treatment of Rheumatoid Arthritis (RA) and related inflammatory conditions, and it possesses significant anti-RA activity. However, the precise molecular mechanisms underlying its therapeutic effects remain to be fully elucidated.</p><p><strong>Aim: </strong>This study aimed to elucidate the therapeutic mechanisms of a Xanthone-enriched Fraction (XRF) from S. inappendiculata against RA by integrating network pharmacology with experimental validation.</p><p><strong>Methods: </strong>Using both an Adjuvant-Induced Arthritis (AIA) rat model and an LPS/IFN-γ- stimulated THP-1 macrophage model, an integrated strategy was implemented. Potential targets and pathways for the xanthones were predicted via network pharmacology and molecular docking. Thereafter, the anti-arthritic efficacy of the xanthone fraction was evaluated in vivo, while its pro-apoptotic effect and regulation of key signaling pathways were investigated in vitro.</p><p><strong>Results: </strong>XRF significantly alleviated joint swelling and synovial inflammation in AIA rats. Mechanistically, it inhibited the PI3K/AKT/mTOR/HIF-1α signaling cascade and reduced phosphorylation of GSK-3β at Ser9, thereby promoting the apoptosis of pro-inflammatory M1 macrophages.</p><p><strong>Discussion: </strong>This work elucidated the anti-RA mechanism of S. inappendiculata: eight xanthones acted on the PI3K/AKT/mTOR/HIF-1α/GSK-3β axis to induce M1 macrophage apoptosis, validating the integrated approach and laying groundwork for RA treatment and natural product research.</p><p><strong>Conclusion: </strong>The present study provides a scientific foundation for further elucidating the mechanisms of S. inappendiculata against RA.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893053","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":"Synthesis and Computational Analysis of Para-fluorinated Phenoxymethyl Benzenesulfonamides as Potential CETP Inhibitors.","authors":"Reema Abu Khalaf, Lana Abu Al-Haj, Rima Hajjo, Mahmoud A Al-Sha'er","doi":"10.2174/0115734099493807260820105853","DOIUrl":"https://doi.org/10.2174/0115734099493807260820105853","url":null,"abstract":"<p><strong>Background: </strong>Dyslipidemia is a major risk factor for the development and progression of coronary artery disease. It results from an abnormal increase in low-density lipoprotein cholesterol, cholesteryl esters, or triglyceride levels in the blood. Cholesteryl ester transfer protein (CETP) is a protein that promotes the bidirectional allocation of triglycerides and cholesteryl esters between lipoproteins in the blood. CETP inhibition reduced dyslipidemia.</p><p><strong>Objective: </strong>This work describes the synthesis, computational characterization, and CETP inhibitory activity of ten para-fluorinated phenoxymethyl benzenesulfonamides (6a-6j).</p><p><strong>Methods: </strong>Compounds 6a-6j were characterized using various spectroscopic techniques and subsequently evaluated using computational analysis and biological assays.</p><p><strong>Results: </strong>The compounds showed in vitro CETP % inhibition ranging from 4.2% to 100% at 10 μM. Among them, compound 6g (para-chloro substituted) demonstrated complete CETP inhibition (100 ± 1.2%), the highest pharmacophore fit value (7.72), and strong docking affinity (LibDock = 144.35; -CDocker = 0.24). Compounds 6e and 6f (ortho- and meta-chloro analogs) also exhibited high inhibitory activity (67% and 72%, respectively) with good docking scores (LibDock: 135.16 and 148.65; fit values: 2.02 and 6.84, respectively). Conversely, the unsubstituted derivative 6a exhibited minimal inhibition (4.2%) and fit/docking scores (LibDock = 140.21; fit = 2.09).</p><p><strong>Discussion: </strong>Cheminformatics, PCA, and descriptor-loading analyses provided insight into the chemical space defined by the descriptors and the structural diversity of the synthesized compounds. Overall, compounds with higher CETP inhibitory activity generally exhibited more favorable docking interactions and higher pharmacophore fit values. Electron-withdrawing chloro substituents may promote favorable interactions within the hydrophobic CETP binding pocket.</p><p><strong>Conclusion: </strong>These findings suggest that electronic effects, substituent orientation, and hydrophobic properties contribute to CETP inhibitory activity, providing a foundation for future optimization and biological evaluation of sulfonamide-based CETP inhibitors.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148893100","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 Therapeutic Mechanism of Puhuaiyin in Rosacea: An Integrated Study Based on Network Pharmacology, Bioinformatics, Molecular Docking, and Molecular Dynamics Simulations.","authors":"Dan Sun, Nana Yang, Yiding Zhao, Wenbin Li, Jing Liu, Weihui Zeng, Youbao Li","doi":"10.2174/0115734099485030260810072506","DOIUrl":"https://doi.org/10.2174/0115734099485030260810072506","url":null,"abstract":"<p><strong>Background: </strong>Rosacea is a chronic inflammatory skin disorder with limited therapeutic options. Puhuaiyin (PHY), a traditional Chinese medicinal formula, shows clinical efficacy, but its multi-component mechanisms remain unclear.</p><p><strong>Methods: </strong>Chemical constituents of PHY were identified by UPLC-Q-TOF-MS. Network pharmacology was used to predict potential targets, which were intersected with rosacea-associated genes. Bioinformatics analyses (differential expression, WGCNA, and machine learning) were applied to the GEO dataset GSE65914 to refine core targets. Molecular docking and molecular dynamics simulations were conducted to validate the binding modes and stability between key active constituents and the core targets.</p><p><strong>Results: </strong>A total of 59 chemical constituents were identified in PHY, with five key active components subsequently screened: quercetin, emodin, kushenol N, physcion, and palmitic acid. Network pharmacology analysis revealed 44 intersecting targets, which were significantly enriched in inflammation-related pathways, such as MAPK, NF-κB, and JAK-STAT signaling. Integrated bioinformatics and machine learning approaches identified MMP9 and IL1B as core targets, both of which were markedly upregulated in rosacea lesions and demonstrated prominent diagnostic value (AUC = 0.999 for MMP9, 0.964 for IL1B). Molecular docking indicated strong binding affinity between the core components and MMP9/IL1B. Molecular dynamics simulations confirmed stable complex conformations over 200 ns, with MM/PBSA binding free energies of -15.54 Kcal/mol (quercetin-MMP9) and -15.57 Kcal/mol (quercetin- IL1B).</p><p><strong>Discussion: </strong>This study, through a multidisciplinary approach, systematically elucidates the \"multi-component, multi-target, and multi-pathway\" mode of action of PHY in the treatment of rosacea. However, the computational predictions remain to be further validated by in vivo and in vitro experiments. Future research should focus on verifying its therapeutic efficacy in animal or cellular models, as well as elucidating the regulatory effects of key active components on the MMP9 and IL1B targets.</p><p><strong>Conclusion: </strong>These computational predictions suggest that PHY may exert therapeutic effects against rosacea via quercetin and other components targeting MMP9 and IL1B, thereby modulating MAPK, NF-κB, and JAK-STAT pathways. The proposed mechanisms include inhibition of inflammation, regulation of the immune microenvironment, attenuation of vascular dilation, and promotion of skin barrier recovery. These findings provide a theoretical basis for future experimental validation.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889783","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":"Mechanism of Shao Kuiling Decoction in Treating Ulcerative Colitis: A Network Pharmacology and Molecular Docking Study.","authors":"Xiaoyun Wu, Zhiguang He, Weitao Ren, Bin Zhao, Yuheng Tseng","doi":"10.2174/0115734099474992260810095531","DOIUrl":"https://doi.org/10.2174/0115734099474992260810095531","url":null,"abstract":"<p><strong>Objective: </strong>Shao Kuiling Decoction (SKD) is used in the long-term management of Ulcerative Colitis (UC), but its molecular basis remains unclear. Because SKD is a multicomponent formula, a computational approach is useful for identifying candidate compounds, targets, and pathways for further validation.</p><p><strong>Methods: </strong>Active compounds in SKD were screened from TCMSP using oral bioavailability and drug-likeness criteria. Putative targets were predicted and intersected with UC-related targets. Shared targets were analyzed by protein-protein interaction network construction, GO/KEGG enrichment, molecular docking, and 100 ns molecular dynamics simulations.</p><p><strong>Results: </strong>A total of 145 active compounds and 94 shared SKD-UC targets were identified. AKT1, TNF, and TP53 were the main hub targets, and the PI3K-Akt and MAPK pathways were the most enriched. Molecular docking showed favorable binding of the major compounds to core targets, with kaempferol‑TNF showing the strongest binding energy of -8.9 kcal/mol and β‑sitosterol‑AKT1 showing -8.4 kcal/mol. Molecular dynamics simulations revealed that the kaempferol‑TNF and β‑sitosterol‑AKT1 complexes remained stable over 100 ns, with RMSD values plateauing at 0.20-0.25 nm and 0.15-0.20 nm, respectively, and maintained consistent hydrogen bonding. In contrast, the acacetin‑TP53 complex showed greater fluctuations, indicating weaker stability.</p><p><strong>Discussion: </strong>These findings suggest that SKD may exert therapeutic effects in UC through coordinated modulation of multiple targets and pathways involved in inflammation and epithelial repair. In a broader context, this study provides a systems-level basis for understanding the potential mechanism of SKD in UC and offers focused directions for future experimental validation.</p><p><strong>Conclusion: </strong>SKD may exert anti-UC effects through a multi-component, multi-target mechanism involving AKT1/TNF/TP53-associated networks and PI3K-Akt/MAPK signaling.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889851","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":"Harnessing the Power of AI: A Modern Review on the Prediction of ADMET Properties in Drug Discovery.","authors":"Satyam Kumar Vishwash, Ram Babu Soni, Ratima Sood, Sourabh Kosey","doi":"10.2174/0115734099467798260807093409","DOIUrl":"https://doi.org/10.2174/0115734099467798260807093409","url":null,"abstract":"<p><p>Drug discovery is frequently limited by high attrition rates, and poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles are a major cause of late-stage failure. Therefore, precise ADMET property prediction is necessary to develop safe and effective drug candidates. Traditional experimental assays and rule-based computational procedures are limited by their poor predictive power, cost, and time, despite providing valuable insights. Innovative strategies to deal with these issues have been introduced by developments in artificial intelligence (AI), such as machine learning (ML), deep learning (DL), graph neural networks (GNNs), generative models, and multi-task learning (MTL). AI techniques can better generalize scaffolds, capture interdependencies between pharmacokinetic and toxicological endpoints, and model complex nonlinear relationships by leveraging large, diverse datasets. Explainable AI (XAI) enhances transparency by detecting biological and structural characteristics that are relevant to predictions, even if integrated pipelines combine predictive modeling with molecular creation and optimization. AI-driven ADMET prediction is becoming a vital tool in lowering attrition, speeding up candidate prioritization, and influencing the direction of rational drug development, despite persistent issues with data quality, regulatory acceptance, and synthetic viability.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148868656","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}