Current computer-aided drug design最新文献

{"title":"Symbolic Fragment Weighting for Substructural Analysis: Genetic Programming vs Genetic Algorithm on ChEMBL.","authors":"Mohd Isrul Esa, Nor Samsiah Sani, Salwani Abdullah","doi":"10.2174/0115734099497745260921104114","DOIUrl":"https://doi.org/10.2174/0115734099497745260921104114","url":null,"abstract":"<p><strong>Introduction: </strong>Substructural Analysis is a scalable and chemically interpretable approach to ligand-based virtual screening, but ranking quality depends strongly on the fragmentweighting model used. This study benchmarks Genetic Programming for evolving symbolic fragment-weighting equations against a Genetic Algorithm baseline that directly optimises integer fragment weights under a fixed Substructural Analysis scoring structure.</p><p><strong>Methods: </strong>Using a ChEMBL-derived benchmark of 15 activity classes represented by MACCS 166-bit fingerprints, we conducted 10 runs per class under a stratified 90/10 train-test protocol. Early-recognition performance was evaluated using enrichment factor at the top 1% (EF@1%) on the test partition. Between-method differences were tested using a two-sided Mann-Whitney U test (α = 0.05), with Benjamini-Hochberg adjustment across activity classes.</p><p><strong>Results: </strong>The Genetic Algorithm baseline achieved higher median EF@1% than the Genetic Programming approach in most activity classes, with statistically significant advantages in 11 of 15 classes before adjustment and 10 of 15 classes after Benjamini-Hochberg adjustment. The Genetic Programming approach remained competitive for COX, 5HT1A, 5HT3, and ACHE; for COX, it produced a higher median EF@1% than the Genetic Algorithm baseline (19.05 vs 16.67), although the difference was not statistically significant (p = 0.333).</p><p><strong>Discussion: </strong>These findings suggest that symbolic fragment-weighting is feasible and inspectable, but that enrichment performance is sensitive to how the optimisation objective is defined for early-recognition screening.</p><p><strong>Conclusion: </strong>Genetic Programming can evolve symbolic Substructural Analysis weighting equations; however, the observed performance gap suggests that future GP fitness designs should be more directly aligned with early-recognition objectives.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225572","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":"Mechanistic Investigation of Linggan Wuwei Jiangxin Decoction for Asthma: Component Analysis, Network Pharmacology, and Experimental Validation.","authors":"Yan Hong, TianCi Fang, YanTong Yu, XueQin Wang, FangFang Sun, Yanquan Han","doi":"10.2174/0115734099484008261009095924","DOIUrl":"https://doi.org/10.2174/0115734099484008261009095924","url":null,"abstract":"<p><strong>Introduction: </strong>Linggan Wuwei Jiangxin decoction (LGD) is a classic herbal formula for alleviating symptoms in patients with asthma. The main functions of LGD are to relieve airway inflammation, alleviate cough, and eliminate phlegm. However, the components of the prescription and the pharmacological mechanisms for treating asthma still need further clarification.</p><p><strong>Objective: </strong>The study provides preliminary insights into the mechanism of action of LGD in treating asthma by analyzing its main components, using network pharmacology and molecular docking, and validating with animal experiments.</p><p><strong>Materials and methods: </strong>Ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) was used to analyze the chemical components of LGD. Network pharmacology and molecular docking were used to predict the relevant targets. Male Sprague-Dawley rats were divided into control, model, LGD (L/M/H) (10.8, 21.6, and 43.2 g/kg/d), and Guilong cough and asthma capsules (GL, positive drug) (2.43 g/kg/d) groups. Intraperitoneal injection of an ovalbumin (OVA) mixture was used to sensitize the rats on days 1 and 7. After sensitization, the rats were exposed to a 1% OVA suspension for 30 min daily for 14 days, starting from day 14. Western blot analysis was used to assess the protein expression of TNF-α, VEGFA, and STAT3.</p><p><strong>Results: </strong>UPLC-Q-TOF-MS identified 44 compounds in LGD. Network pharmacology and molecular docking identified core target proteins including TNF-α, VEGFA, and STAT3. The experiments revealed that LGD reduces the serum levels of the inflammatory markers interleukin- 4 (IL-4), immunoglobulin E (IgE), and interferon-γ (IFN-γ) in asthmatic rats and affects energy metabolism. Additional studies revealed that LGD could inhibit the expression of TNF-α, VEGFA, and STAT3 proteins.</p><p><strong>Conclusions: </strong>This preliminary study indicates that LGD ameliorates asthma in rats by inhibiting the expression of TNF-α, VEGFA, and STAT3 proteins. More in vitro experiments are required to clarify the effects of LGD on these target proteins.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149221200","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 and Network Pharmacology Identification of Acacia senegal Phytochemicals as Potential α-Amylase Inhibitors.","authors":"Abdulkadir Abdu, Hossamaldeen Bakrey, Hind A Rikabi, Amani Ahmed Osman Ali, Israa Abdelrahim, Eman M Abdulkareem, Lina Eltaib","doi":"10.2174/0115734099489718260910060820","DOIUrl":"https://doi.org/10.2174/0115734099489718260910060820","url":null,"abstract":"<p><strong>Introduction: </strong>Diabetes mellitus is a rapidly expanding chronic disease, prompting interest in plant-derived natural products as adjuncts to conventional therapy because of their improvements in glycaemic control and reduced complications. To evaluate phytochemicals' potential as α-amylase inhibitors using an integrated in-silico workflow.</p><p><strong>Methods: </strong>Acacia senegal phytochemicals retrieved from the IMPPAT 2.0 database were evaluated their in-silico studies against α-amylase inhibitors using molecular docking, DFT, pharmacokinetic and toxicity profiling, in addition to PASS prediction, network pharmacology and MD simulations.</p><p><strong>Results: </strong>Docking of 31 compounds was carried out, and the top three ligands, which are: IMPHY014840 (-9.321 kcal/mol), IMPHY014838 (-9.208 kcal/mol) and IMPHY006972 (-9.026 kcal/mol), were selected. Their favorable binding energies indicated strong predicted affinity toward the selected enzyme target. DFT calculations showed identical HOMO-LUMO gaps of 6.936 eV for the first two ligands, whereas the third one displayed a larger gap of 9.484 eV, indicating lower chemical reactivity. Furthermore, pharmacokinetic screening indicated high human intestinal absorption (75-86 %) and favorable Caco-2 permeability for the top three compounds, particularly for IMPHY014840 and IMPHY014838. PASS analysis suggested protease target affinity for IMPHY014840 and GPCR/transcription factor activity for IMPHY014838, expanding their therapeutic scope beyond α-amylase inhibition. MD trajectories confirmed overall complex stability, with IMPHY014838 exhibiting marginally lower ligand RMSD and therefore slightly superior dynamical stability.</p><p><strong>Discussion: </strong>The present study identified IMPHY014840 and IMPHY014838 as promising α- amylase inhibitors, with docking scores of -9.321 and -9.208 kcal/mol, respectively. DFT, ADMET, toxicity, PASS, and 100 ns MD simulations indicate stable enzyme-ligand binding, high predicted intestinal absorption, and multitarget potential. Network pharmacology further suggests that these compounds act on a broad set of diabetes related targets, supporting their value as antidiabetic lead candidates.</p><p><strong>Conclusion: </strong>The combined computational evidence supports IMPHY014840 and IMPHY014838 as promising natural α-amylase inhibitors for type 2 DM management, and recommends in vitro, in vivo, and formulation optimization to address their limitations.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187060","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":"NNGeoGVS: A Unified Equivariant Graph Neural Network Framework for Integrated Structure and Ligand-based Virtual Screening.","authors":"Suresh Chand Yadam, Phani Kumar Singamsetty","doi":"10.2174/0115734099527526261007151436","DOIUrl":"https://doi.org/10.2174/0115734099527526261007151436","url":null,"abstract":"<p><strong>Introduction: </strong>Virtual screening (VS) is a widely used computational strategy in early- stage drug discovery for identifying bioactive compounds from large chemical libraries. Although structure-based and ligand-based approaches have demonstrated success individually, their integration into a unified deep learning framework remains limited. This study presents NNGeoGVS (Neural Network Geometric Graph-based Virtual Screening), an Equivariant Graph Neural Network (EGNN)-based framework that integrates ligand molecular topology and protein active-site geometry for activity prediction.</p><p><strong>Methods: </strong>NNGeoGVS represents ligands and protein binding sites as molecular graphs and learns structure-activity relationships through end-to-end message passing using PyTorch Geometric. The framework combines node-level atomic features, edge-level bond descriptors, and global geometric and pharmacophore descriptors. Active-site geometry is characterized using Ultra-Fast Shape Recognition (USR) and convex hull algorithms. These features are integrated through a multi-branch GNN architecture with late fusion, while focal loss optimization and graph-level augmentation address class imbalance.</p><p><strong>Results: </strong>NNGeoGVS was evaluated on the DUD-E dataset (8 kinase targets) and the LITPCBA benchmark (15 targets). Compared with NNGeoAS and other GNN-based methods, it achieved average EF1% values of 35.92 and 17.03 and ROC-AUC scores of 0.98 and 0.77 on DUD-E and LIT-PCBA, respectively.</p><p><strong>Discussion: </strong>The integration of ligand topology with protein active-site geometry improves enrichment and classification performance, demonstrating the effectiveness of combining complementary structural information within a unified EGNN framework.</p><p><strong>Conclusion: </strong>We proposed NNGeoGVS, a robust framework for virtual screening that integrates structural information of both proteins and ligands and demonstrated robust predictive performance on benchmark datasets.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187010","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 Bioactive Compounds Targeting Conserved MSCRAMMs Adhesion Proteins to Disrupt Staphylococcus aureus Host Colonization.","authors":"Irfa Asghar, Usama Bin Asghar, Syed Basit Shah, Samavia Akhter, Asim Ali, Shakir Ullah, Muhammad Tanveer Alam, Faryal Khan, Sajid Ali, Muhammad Ali","doi":"10.2174/0115734099494916260910051136","DOIUrl":"https://doi.org/10.2174/0115734099494916260910051136","url":null,"abstract":"<p><strong>Introduction: </strong>Colonization by Staphylococcus aureus is a key event preceding invasive infections. During host interaction, the bacterium expresses Microbial Surface Components Recognizing Adhesive Matrix Molecules (MSCRAMMs), a family of surface-associated adhesins that mediate attachment to extracellular matrix components and promote persistence. Targeting conserved MSCRAMM proteins may provide a preventive strategy to limit bacterial establishment.</p><p><strong>Materials and methods: </strong>A total of 249,079 bioactive compounds were compiled from the published literature and public databases. Following screening for drug-likeness and pharmacokinetic properties, 1,600 compounds were shortlisted. Core-genome analysis of MSCRAMM proteins was conducted using panX to identify conserved targets. Druggability and safety profiling were performed using the Target-Pathogen database and sequence similarity analysis against the human proteome to minimize off-target effects. Selected compounds were subjected to molecular docking, inhibition constant estimation, and molecular dynamics simulations to evaluate binding affinity and complex stability.</p><p><strong>Results: </strong>Among the 24 MSCRAMM proteins identified, six conserved proteins were prioritized as potential therapeutic targets. All demonstrated favorable druggability characteristics and minimal similarity to human proteins. Docking analysis revealed 18 compounds with strong binding affinities, of which 12 were non-hepatotoxic and exhibited stable interactions with multiple targets. Molecular dynamics simulations confirmed the structural integrity and sustained binding of the selected protein-ligand complexes.</p><p><strong>Discussion: </strong>These findings highlight the potential of targeting conserved MSCRAMM proteins as an anti-virulence approach and provide a basis for the future development of alternative strategies against antibiotic-resistant S. aureus.</p><p><strong>Conclusion: </strong>The identified compounds represent promising candidates for further experimental validation aimed at preventing S. aureus colonization.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187087","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":"Revolution of Computer-assisted Drug Design: The Transformative Role of Artificial Intelligence.","authors":"Uma Rawat, Anita Singh","doi":"10.2174/0115734099491620260915174557","DOIUrl":"https://doi.org/10.2174/0115734099491620260915174557","url":null,"abstract":"<p><p>Artificial intelligence (AI) is transforming computer-aided drug design (CADD) by enabling more rapid, efficient, and data-driven approaches to drug discovery. This narrative review examines recent applications of AI, machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (NLP), and generative AI across major stages of the drug discovery pipeline. Literature published primarily between 2021 and 2026 was reviewed to describe advances in AI-assisted target identification, protein structure and protein- protein interaction prediction, virtual screening, molecular docking, quantitative structure- activity relationship (QSAR) modelling, drug repurposing, ADMET prediction, biomarker discovery, lead optimization, and de novo molecular design. The review also highlights AI-enabled computational tools, databases, and predictive models that support the analysis of large chemical and biological datasets. AI-based approaches can improve prediction accuracy, accelerate candidate identification and optimization, and reduce the time and cost associated with conventional drug discovery workflows. However, widespread implementation remains limited by challenges including data quality and bias, limited model generalizability, insufficient interpretability and reproducibility, computational demands, and regulatory and ethical concerns. AI should therefore be considered a complementary technology that supports, rather than replaces, experimental and clinical validation. Future research should focus on developing standardized and diverse datasets, explainable and transparent AI models, robust external validation strategies, and improved integration of multimodal and multi-omics data. Furthermore, the convergence of AI with emerging technologies, including quantum computing, generative models, automated laboratories, and precision medicine, may further advance CADD. Collaborative efforts among computational scientists, medicinal chemists, biologists, clinicians, and regulatory agencies will be essential to translate AI-driven discoveries into safe, effective, and clinically relevant therapeutic candidates.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120839","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":"Integrated LC-MS and Computer-aided Drug Design Evaluation of <i>Sida cordifolia</i> Herbal Microemulsion Targeting Inflammatory Enzymes through Molecular Docking, MM-GBSA, DFT, and ADMET Profiling.","authors":"Kalyani Asgaonkar, Amruta Avalaskar, Kalirajan Rajagopal, Krishna Shevate, Shital Patil, Trupti Chitre, Gajanan Rathod, Pranaya Nawale, Varun Hambir, Prachi Divate","doi":"10.2174/0115734099498434260828063631","DOIUrl":"https://doi.org/10.2174/0115734099498434260828063631","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Sida cordifolia (SC) is traditionally used for treat inflammatory disorders; however, its molecular composition and the mechanistic basis of action remain poorly characterized. This study aimed to identify its bioactive constituents and elucidate their multi-target anti-inflammatory potential using an integrated computational and experimental approach.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Phytochemical profiling of the ethanolic extract was performed using LC-MS. The identified phytoconstituents were evaluated for their pharmacokinetic and toxicity profiles using in silico ADMET prediction. Molecular docking was carried out against secretory phospholipase A2 (sPLA2), 5-lipoxygenase (5-LOX), and cyclooxygenase-2 (COX-2), followed by MMGBSA binding free energy calculations, density functional theory (DFT) analysis, and molecular dynamics (MD) simulations for selected COX-2 complexes. An oil-in-water microemulsion of the extract was formulated and characterized, followed by in vitro (protein denaturation and membrane stabilization) and in vivo (carrageenan-induced paw edema) evaluation of antiinflammatory activity.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;LC-MS analysis identified thirteen phytoconstituents belonging to quinazoline alkaloids, flavonoids, fatty acids, phytosterols, and ecdysteroids. ADMET analysis predicted favorable drug-likeness and low toxicity for most compounds. Molecular docking revealed strong multi-target interactions of quinazoline alkaloids and prenylated flavonoids with sPLA2, 5-LOX, and COX-2. MM-GBSA analysis demonstrated favorable binding free energies, with 5-hydroxy-3-isoprenyl flavone (-47.31 kcal/mol) and Vasicinol (-44.10 kcal/mol) exhibiting the strongest affinity toward COX-2, while Vasicinol maintained favorable binding across all three inflammatory targets. DFT analysis showed lower HOMO-LUMO energy gaps and higher softness for Vasicinol and 5-hydroxy-3-isoprenyl flavone, indicating enhanced electronic reactivity. Molecular dynamics simulations demonstrated superior dynamic stability of the COX-2- Vasicinone complex, characterized by stable protein and ligand RMSD profiles, minimal fluctuations of active-site residues, persistent interactions with key catalytic residues, and consistent thermal MM-GBSA binding energies throughout the 100 ns simulation. The formulated microemulsion exhibited desirable physicochemical characteristics and significantly enhanced antiinflammatory activity in both in vitro and in vivo models.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The integrated computational analyses demonstrated that quinazoline alkaloids and flavonoids possess complementary binding characteristics toward multiple inflammatory targets. Docking, MM-GBSA, DFT, and MD simulations collectively indicated that favorable binding energetics, electronic properties, and dynamic stability contribute to the inhibitory potential of these phytoconstituents. Furthermore, incorporation of the extract into a microemu","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149043004","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 Tubulin Dimer Inhibitors for Pancreatic Cancer Using an Integrated Computational Approach.","authors":"Hemantha Mani Kumar Chakravarthi Chanda, Sudheer Kumar Katari","doi":"10.2174/0115734099490104260914114511","DOIUrl":"https://doi.org/10.2174/0115734099490104260914114511","url":null,"abstract":"<p><strong>Introduction: </strong>Despite advances in medical science, pancreatic cancer is still among the deadliest forms of cancer, and only 10% of patients survive because they are usually diagnosed late and do not respond to standard treatments. Inhibition of microtubule behaviour by disrupting their dimeric structures is a potential treatment for pancreatic cancer. This study aims to identify new inhibitors that will bind to the tubulin dimer using an integrative computational drug discovery approach.</p><p><strong>Materials and methods: </strong>An advanced series of in silico experiments was conducted using molecular docking, Density Functional Theory (DFT), Molecular Dynamics (MD) simulations, and Principal Component Analysis (PCA). Compounds from DrugBank were selected based on their strong interactions with the tubulin dimer. Moreover, DFT was utilized to investigate electronic interactions, whereas MD simulations (1 μs) were used to understand the stability and behaviour of the studied complex.</p><p><strong>Results: </strong>The molecules Bisphosphonate-8 and Fenoldopam were selected as inhibitors, whereas Paclitaxel was chosen as a reference compound. Analysis of docking and binding free energy showed that Bisphosphonate-8 has approximately twice the binding affinity for the α-β tubulin dimer compared to Paclitaxel. The DFT calculations indicated a high interaction capacity. MD simulation showed that Bisphosphonate-8 forms a stable complex during the simulation, whereas Paclitaxel demonstrated notable instability. Principal component analysis corroborated that Bisphosphonate-8 is highly adaptable and stable.</p><p><strong>Discussion: </strong>This computational approach successfully identified Bisphosphonate-8 as an inhibitor of the α-β tubulin dimer, with better binding affinity and stability than well-known compounds and desirable pharmacokinetic properties.</p><p><strong>Conclusion: </strong>Based on the results presented above, Bisphosphonate-8 is a promising candidate for targeting the α-β tubulin dimer in pancreatic cancer, with stronger interactions and greater conformational adaptability. The computational data provide an excellent starting point for further experiments aimed at developing novel therapeutic approaches to target the α-β tubulin heterodimer in pancreatic cancer.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149187094","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":"A Mechanistic In-silico Approach for Design and Evaluation of Novel (1H-indol-3-yl) Acetic Acid Analogues as Dual-action Anti-retroviral and Anti-bacterial Agents: A Foresight into Rational Drug Design Strategies.","authors":"Sayan Bhattacharjee, Rahul Ghosh, Biplab Debnath, Swastika Ganguly","doi":"10.2174/0115734099492629260908062205","DOIUrl":"https://doi.org/10.2174/0115734099492629260908062205","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;The pressing need for effective HIV-1 Reverse Transcriptase (HIV-1 RT) inhibitors and for agents that suppress Glucosamine-6-Phosphate (GlcN6P) is a significant challenge in combating viral infections and related diseases. Indole acetic acid analogues have demonstrated remarkable versatility in modulating both anti-viral and GlcN6P pathways, but potential dual-action inhibitors remain unexplored. In this research, we have taken an interdisciplinary in-silico approach to develop and evaluate a set of novel indole analogues as potential anti-retroviral and GlcN6P agents. The ability of indole acetic analogues to exhibit dual-action antiretroviral and antibacterial efficacy through inhibition of viral replication at various stages and bacterial cell wall biosynthesis, attributed to the presence of an aryl carboxylic group that enhances the favorability of hydrophobic interactions, van der Waals interactions, and π-π stacking, has made them promising scaffolds in drug discovery.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;To validate these findings, we determined the binding modalities of the 1m analogue with several key proteins involved in glucosamine metabolism and viral replication using in silico molecular docking evaluations comprising molecular dynamics simulations, binding free energy calculations (MM-GBSA), ADMET predictions, trajectory analysis, and DFT calculations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Molecular dynamics studies of the most potent analogue 1m were performed over 300 ns, yielding MM-GBSA values of -71.96 ± 5.29 kcal/mol and -67.45 ± 6.16 kcal/mol for HIV-1 RT (PDB: 1RT2) and Glucosamine-6-phosphate (PDB: 2VF5), respectively. We also subjected compound 1m to predictive ADMET analysis to determine its pharmacokinetic characteristics and potential safety profile. Our results, carried out in parallel, showed that compound 1m mediated favourable binding events, forming long-lasting, stable complexes, and exhibited high binding affinities for the target proteins, comparable to those of the standard drugs Efavirenz and Ciprofloxacin.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The promising nature of compound 1m as a drug-like molecule was highlighted in anticipatory ADMET studies, supporting further evaluation as a potential dual-action inhibitor of glucosamine-6-phosphate synthase and an antiretroviral agent, given its favourable drug-like properties. To summarise, the strategy of calculations, including molecular docking, dynamic simulations, MM-GBSA calculations, predictive ADMET studies and DFT calculations, provides information about the design and assessment of potent indole analogues as dual-action anti-retroviral and GlcN6P agents.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Our comprehensive in silico research, comprising molecular docking, molecular dynamics simulations following MM-GBSA analysis, predictive ADMET analysis, trajectory analysis, and DFT studies, revealed strong binding affinity to amino acids, several hyd","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149013385","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":"Repurposing Lenalidomide for Refractory Behcet's Disease: Systematic Review and Computational Pharmacology Insights for Rare Disease Therapy.","authors":"Chengzhen Lyu, Shuai Li, Kun He, Ziqi Guo, Rou Tang, Wangyang Chen, Weiyang Zheng, Dong Wu","doi":"10.2174/0115734099491715260903114308","DOIUrl":"https://doi.org/10.2174/0115734099491715260903114308","url":null,"abstract":"<p><strong>Introduction: </strong>Lenalidomide is an immunomodulatory drug. Its repurposing for refractory Behcet's Disease is a promising strategy. Behcet's Disease is a rare and complex autoinflammatory condition. Current treatment options are limited. This study explored the drug's therapeutic potential using clinical and computational analyses.</p><p><strong>Methods: </strong>This study included a systematic review of clinical case reports. Computational methods included toxicological predictions, network pharmacology to screen common targets of lenalidomide and Behcet's Disease, and the construction of protein-protein interaction networks utilizing tools such as ProTox-3.0, ADMETlab 3.0, and Cytoscape. Additionally, molecular docking (AutoDock Vina) and molecular dynamics simulations (GROMACS) were used to assess binding and stability.</p><p><strong>Results: </strong>Clinical data showed low-dose lenalidomide provided rapid symptom relief and appeared to be well tolerated. Computational toxicology revealed a complex, dose-dependent safety profile. Network analysis identified 63 common targets. Seven core genes were found, including IL6, TNF, and JAK1. These genes were enriched in immune pathways, particularly the JAK-STAT cascade. Molecular docking and dynamics confirmed stable binding to key targets like IL6 and JAK1.</p><p><strong>Discussion: </strong>Lenalidomide appears potentially effective in Behcet's Disease. Its action likely involves multi-target regulation of core immune pathways. Multi-target regulation of the JAKSTAT and TNF signaling cascades directly addresses the core autoinflammatory pathology of Behcet's Disease. The concordance between clinical efficacy and computational findings highlights its repurposing potential. The findings also stress the importance of dose optimization for safety, generating a promising low-dose safety hypothesis.</p><p><strong>Conclusion: </strong>Lenalidomide is a viable repurposing candidate warranting further clinical investigation for refractory Behcet's Disease. This strategy provides a mechanistically supported treatment for this rare disease. Combining network pharmacology and molecular dynamics provides a robust preclinical rationale for repurposing lenalidomide in Behcet's Disease.</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":"148972769","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}