Search Results (126)

Drug discovery is an intricate and resource-intensive process in which computational approaches, such as molecular docking, are essential, particularly in the early stages, to identify potential hits. However, docking still has many drawbacks, including problems in managing protein flexibility and the reliability of scoring functions. In this paper, we systematically compared the performance of AutoDock Vina, one of the most widely used open-source docking tools, with GNINA. This advanced evolution integrates convolutional neural networks (CNNs) for pose scoring. The comparison was conducted on ten heterogeneous protein targets, including metalloenzymes, kinases, and G-protein-coupled receptors (GPCRs). With the ability to accurately replicate binding poses and their energy values, GNINA showed outstanding performance in both virtual screening (VS) of active ligands and re-docking steps of co-crystallized ligands. GNINA’s enhanced ability to accurately distinguish between true positives and false positives—a specificity not found with AutoDock Vina—is confirmed by ROC curves and Enrichment Factor (EF) results. Therefore, we propose an integrated GNINA-based workflow that can significantly enhance the quality and reliability of docking results, providing a valuable tool for optimizing the initial stages of drug discovery. Full article

The convergence of artificial intelligence (AI) and nanomedicine has transformed cancer vaccine development, particularly in optimizing RNA-loaded lipid nanoparticles (LNPs). Stability and targeted delivery are major obstacles to the clinical translation of promising RNA-LNP vaccines for cancer immunotherapy. This systematic review analyzes the AI’s impact on LNP engineering through machine learning-driven predictive models, generative adversarial networks (GANs) for novel lipid design, and neural network-enhanced biodistribution prediction. AI reduces the therapeutic development timeline through accelerated virtual screening of millions of lipid combinations, compared to conventional high-throughput screening. Furthermore, AI-optimized LNPs demonstrate improved tumor targeting. GAN-generated lipids show structural novelty while maintaining higher encapsulation efficiency; graph neural networks predict RNA-LNP binding affinity with high accuracy vs. experimental data; digital twins reduce lyophilization optimization from years to months; and federated learning models enable multi-institutional data sharing. We propose a framework to address key technical challenges: training data quality (min. 15,000 lipid structures), model interpretability (SHAP > 0.65), and regulatory compliance (21CFR Part 11). AI integration reduces manufacturing costs and makes personalized cancer vaccine affordable. Future directions need to prioritize quantum machine learning for stability prediction and edge computing for real-time formulation modifications. Full article

Phase-sensitive surface plasmon resonance (SPR) detection is widely employed in molecular dynamics studies and SPR imaging owing to its real-time capability, high sensitivity, and compatibility with imaging systems. A key research objective is to achieve higher measurement resolution of refractive index under optimal dynamic range conditions. We present an enhanced SPR phase imaging system combining a quad-polarization filter array for phase differential detection with a novel polarization pair, block matching, and 4D filtering (PPBM4D) algorithm to extend the dynamic range and enhance resolution. By extending the BM3D framework, PPBM4D leverages inter-polarization correlations to generate virtual measurements for each channel in the quad-polarization filter, enabling more effective noise suppression through collaborative filtering. The algorithm demonstrates 57% instrumental noise reduction and achieves 1.51 × 10⁻⁶ RIU resolution (1.333–1.393 RIU range). The system’s algorithm performance is validated through stepwise NaCl solution switching experiments (0.0025–0.08%) and protein interaction assays (0.15625–20 μg/mL). This advancement establishes a robust framework for high-resolution SPR applications across a broad dynamic range, particularly benefiting live-cell imaging and high-throughput screening. Full article

Lactate dehydrogenase (LDH) catalyzes the reversible interconversion of pyruvate and lactate, coupled with the redox cycling of NADH and NAD⁺. While LDHA has been extensively studied as a therapeutic target, particularly in cancer, due to its role in the Warburg effect, LDHB remains underexplored, despite its involvement in the metabolic reprogramming of specific cancer types, including breast and lung cancers. Most known LDH inhibitors are designed against the LDHA isoform and act competitively at the active site. In contrast, LDHB exhibits distinct kinetic properties, substrate preferences, and structural features, warranting isoform-specific screening strategies. In this study, 115 natural compounds previously reported as LDHA inhibitors were systematically evaluated for LDHB inhibition using an integrated in silico and in vitro approach. Virtual screening identified 16 lead phytochemicals, among which luteolin and quercetin exhibited uncompetitive inhibition of LDHB, as demonstrated by enzyme kinetic assays. These findings were strongly supported by molecular docking analyses, which revealed that both compounds bind at an allosteric site located at the dimer interface, closely resembling the binding mode of the established LDHB uncompetitive inhibitor AXKO-0046. In contrast, comparative docking against LDHA confirmed their active-site binding and competitive inhibition, underscoring their isoform-specific behavior. Our findings highlight the necessity of assay conditions tailored to LDHB’s physiological role and demonstrate the application of a previously validated colorimetric assay for high-throughput screening. This work lays the foundation for the rational design of selective LDHB inhibitors from natural product libraries. Full article

The Chikungunya virus (CHIKV) continues to pose a significant global health challenge due to the absence of effective antiviral treatments and limited vaccine availability. This study employed a comprehensive in silico workflow, incorporating high-throughput virtual screening, binding free-energy calculations, ADMET (absorption, distribution, metabolism, excretion, and toxicity) analysis, and 200 ns molecular dynamics (MD) simulations, to identify new inhibitors targeting the E1–E2 glycoprotein complex, crucial for CHIKV entry and membrane fusion. Four promising candidates were identified from a library of 20,000 compounds, with CID 136801451 showing the most potent binding (docking score: −10.227; ΔG_bind: −51.53 kcal/mol). The top four compounds exhibited favorable ADMET profiles, meeting nearly all criteria. MD simulations confirmed stable binding and strong interactions between CID 136801451 and the E1–E2 complex, evidenced by consistently low RMSD values. These findings highlight CID 136801451 as a promising CHIKV entry inhibitor, warranting further in vitro and in vivo evaluation to advance the development of effective anti-CHIKV therapeutics. Full article

Background: Tankyrase (TNKS1) regulates the WNT/β-catenin pathway, while CDK8 is a transcriptional regulator overexpressed in renal cell carcinoma (RCC). This study aims to identify novel dual inhibitors of tankyrase and Cyclin-dependent kinase 8 (CDK8), utilizing bioinformatics and in vitro methods and to assess their efficiency in renal cancer cells. Methods: To identify leads, the ChemBridge library was screening using high-throughput virtual screening (HTVS), which was followed by protein–ligand interaction analysis, Molecular Dynamics (MD) simulation, and Gibbs binding free energy estimation. A-498, Caki-1, and HK-2 cells were employed to validate in vitro efficacy. Results: TCS9725 was discovered by HTVS with binding affinities of −8.1 kcal/mol and −8.2 kcal/mol for TNKS1 and CDK8, respectively. TCS9725 had robust binding interactions with root mean square deviation values of 0.00 nm. The ΔG binding estimate was −27.45 for TNKS1 and −27.88 for CDK8, respectively. ADME predictions favored specific small-molecule inhibition profiles. TCS9725 reduced TNKS1 and CDK8 activities with IC50s of 243 nM and 403.6 nM, respectively. The compound efficiently inhibited the growth of A-498 and Caki-1 cells with GI50 values of 385.9 nM and 243.6 nM, respectively, with high selectivity compared to the non-cancerous kidney cells. TCS9725 decreased STAT1 and β-catenin positivity in A-498 and Caki-1 cells. The compound induced apoptosis and reduced TGFβ-stimulated trans-endothelial migration and p-smad2/3 signaling in both RCC cells. Conclusions: This work provides valuable insights into the therapeutic potential of TCS9725, a dual inhibitor of TNKS1 and CDK8. Further developments of this molecule could lead to new and effective treatments for this devastating disease. Full article

Background: Ischemic stroke (IS) is a severe condition with limited therapeutic options. Pyroptosis, a type of programmed cell death linked to inflammation, is closely associated with IS-related damage. Studies suggest inflammation aligns with the traditional Chinese medicine (TCM) concept of “fire-heat syndrome”. Huanglian Jiedu Decoction (HLJD), a TCM formula known for clearing heat and purging fire, has shown therapeutic effects on IS, potentially by regulating pyroptosis. Study design: Eight-week-old male mice were divided into six groups: sham operation, model, positive drug, and low-, medium-, and high-dose HLJD groups. After a week of adaptive feeding, mice received respective treatments for five days, followed by modeling on the sixth day, with samples collected 23 h post-perfusion. Analyses included multi-omics, physiology, histopathology, virtual drug screening, target affinity assessment, and molecular biology techniques to measure relevant indicators. Results: HLJD effectively mitigated IS-related damage, maintaining neurological function, reducing ischemic levels, protecting cellular morphology, inhibiting neuronal apoptosis, and preserving blood–brain barrier integrity. Bioinformatics of high-throughput omics data revealed significant activation of pyroptosis and related inflammatory pathways in IS. ScRNA-seq identified neutrophils, macrophages, and microglia as key pyroptotic cell types, suggesting potential therapeutic targets. Network pharmacology and molecular docking identified NLRP3 as a critical target, with 6819 ligand–receptor docking results. SPR molecular fishing, LC-MS, molecular dynamics, and affinity measurements identified small molecules with high affinity for NLRP3. Molecular biology techniques confirmed that HLJD regulates pyroptosis via the classical inflammasome signaling pathway and modulates the inflammatory microenvironment. Conclusions: Following IS, pyroptosis in myeloid cells triggers an inflammatory cascade, leading to neural damage. HLJD may inhibit NLRP3 activity, reducing pyroptosis and associated inflammation, and ultimately mitigating damage. Full article

The prostaglandin E2 receptor EP3 is emerging as a promising therapeutic target in cardiovascular diseases because of its involvement in vascular inflammation, platelet aggregation, and vasoconstriction. However, selective EP3 ligands with validated biological activities are scarce. In this study, we combined computational and experimental strategies to identify and validate novel EP3 receptor ligands with therapeutic potential. We implemented a high-throughput, structure- and ligand-based virtual screening pipeline, enabling efficient exploration of approved drugs and natural compounds from DrugBank and FooDB libraries. Top-scoring candidates were prioritised based on binding energy and pharmacophoric similarity. Selected hits were subjected to in silico ADME/Tox profiling using QikProp to identify molecules with favourable pharmacokinetic and safety parameters. TUCA, masoprocol, and pravastatin sodium have emerged as lead candidates and were validated in vitro using endothelial migration and platelet aggregation assays. TUCA exhibited the most consistent inhibitory effect on endothelial migration, whereas masoprocol and hydrocortisone significantly reduced platelet aggregation. These findings establish a multidimensional workflow for the rational identification of EP3 ligands and support their potential use in cardiovascular therapeutics. Full article

Background/Objectives: The increasing threat of antibiotic resistance and the declining efficiency of traditional drug discovery pipelines highlight the urgent need for novel drug targets and effective enzyme inhibitors against infectious diseases. Oligopeptidase B (OPB), a serine protease with trypsin-like specificity that processes low-molecular-weight peptides and oligopeptides, is present in bacteria and certain parasites but absent in mammals. This unique distribution makes OPB an attractive and selective target for antimicrobial drug development. Methods: Three-dimensional models of OPB from Serratia marcescens and Stenotrophomonas maltophilia, previously identified by our research group, were constructed via homology modeling using the best available OPB template from the RCSB Protein Data Bank. The S. marcescens OPB model was subjected to high-throughput virtual screening (HTVS) against the Natural Products Atlas (npatlas) database. Top-ranking compounds were further evaluated using Glide standard precision (SP) and extra precision (XP) docking protocols. Binding affinities were refined using molecular mechanics with generalized born and surface area (MM–GBSA) calculations. Molecular dynamics (MD) simulations assessed binding stability, while absorption distribution metabolism excretion and toxicity (ADMET) profiling evaluated drug-likeness and pharmacokinetic properties. Results: Ten natural product compounds demonstrated stronger binding affinities than antipain, a well-known oligopeptide-based protease inhibitor, as indicated by their more favorable MM–GBSA scores of −60.90 kcal/mol (S. marcescens) and −27.07 kcal/mol (S. maltophilia). Among these, dichrysobactin and validamycin E consistently exhibited favorable binding profiles across both OPB models. MD simulations confirmed the stability of their interactions with OPB active sites, maintaining favorable binding conformations throughout the simulation period. ADMET analysis suggested that while both compounds show promise, lead optimization is required to enhance their drug-like characteristics. Conclusions: This study identifies dichrysobactin and validamycin E as promising OPB inhibitors with potential antimicrobial activity. These findings support their further development as selective and potent agents against bacterial pathogens, including resistant strains, and underscore the need for experimental validation to confirm their efficacy and safety. Full article

Transient receptor potential vanilloid 4 (TRPV4) is a calcium-permeable cation channel critical for maintaining intracellular Ca²⁺ homeostasis and is essential in regulating immune responses, metabolic processes, and signal transduction. Recent studies have shown that TRPV4 activation enhances influenza A virus infection, promoting viral replication and transmission. However, there has been limited exploration of antiviral drugs targeting the TRPV4 channel. In this study, we developed the first machine learning model specifically designed to predict TRPV4 inhibitory small molecules, providing a novel approach for rapidly identifying repurposed drugs with potential antiviral effects. Our approach integrated machine learning, virtual screening, data analysis, and experimental validation to efficiently screen and evaluate candidate molecules. For high-throughput virtual screening, we employed computational methods to screen open-source molecular databases targeting the TRPV4 receptor protein. The virtual screening results were ranked based on predicted scores from our optimized model and binding energy, allowing us to prioritize potential inhibitors. Fifteen small-molecule drugs were selected for further in vitro and in vivo antiviral testing against influenza. Notably, glecaprevir and everolimus demonstrated significant inhibitory effects on the influenza virus, markedly improving survival rates in influenza-infected mice (protection rates of 80% and 100%, respectively). We also validated the mechanisms by which these drugs interact with the TRPV4 channel. In summary, our study presents the first predictive model for identifying TRPV4 inhibitors, underscoring TRPV4 inhibition as a promising strategy for antiviral drug development against influenza. This pioneering approach lays the groundwork for future clinical research targeting the TRPV4 channel in antiviral therapies. Full article

Solid-state electrolytes (SSEs), as key materials for all-solid-state batteries (ASSBs), face challenges such as low ionic conductivity and poor interfacial stability. With the rapid advancement of computational science and artificial intelligence (AI) technologies, theoretical calculations and AI methods are emerging as efficient and important virtual tools for predicting and screening high-performance SSEs. To further promote the development of the SSEs, this review outlines recent applications of theoretical calculations and AI in this field. First, the current applications of theoretical calculation methods, such as density functional theory (DFT) and molecular dynamics (MD), in material structure optimization, electronic property analysis, and ionic transport dynamics are introduced, along with an analysis of their limitations. Second, innovative applications of AI methods, including machine learning (ML) and deep learning (DL), in predicting material properties, analyzing structural features, and simulating interfacial behaviors are elaborated. Subsequently, the synergistic application strategies combining high-throughput screening (HTS), theoretical calculations, and AI methods are highlighted, demonstrating the unique advantages of integrating multiple methodologies in material discovery and performance optimization. Finally, the current research progress is summarized, and future development trends are forecasted. The deep integration of theoretical calculations and AI methods is expected to significantly accelerate the development of high-performance SSE materials, thereby driving the industrial application of ASSBs. Full article

Ulcerative colitis (UC) is a chronic inflammatory condition that affects the intestines. Research has shown that reducing the activity of DDR1 can help maintain intestinal barrier function in UC, making DDR1 a promising target for treatment. However, the development of DDR1 inhibitors as drugs has been hindered by issues such as toxicity and poor binding stability. As a result, there are currently no DDR1-targeting drugs available for clinical use, highlighting the need for new inhibitors. In a recent study, a dataset of 85 DDR1 inhibitors was analyzed to identify key characteristics for effective inhibition. A pharmacophore model was constructed and validated to screen a library of marine natural products for potential DDR1 inhibitors. Through high-throughput virtual screening and precise docking, 17 promising compounds were identified from a pool of over 52,000 molecules in the marine database. To improve binding affinity and reduce potential toxicity, scaffold hopping was employed to modify the 17 compounds, resulting in the generation of 1070 new compounds. These new compounds were further evaluated through docking and ADMET analysis, leading to the identification of three compounds—39713a, 34346a, and 34419a—with superior predicted activity and drug-like properties compared to the original 17 compounds. Further analysis showed that the binding free energy values of the three candidate compounds were less than −12.200 kcal/mol, which was similar to or better than −12.377 kcal/mol of the known positive compound VU6015929, and the drug-like properties were better than those of the positive compounds. Molecular dynamics simulations were then conducted on these three candidate compounds, confirming their stable interactions with the target protein. In conclusion, compounds 39713a, 34346a, and 34419a show promise as potential DDR1 inhibitors for the treatment of ulcerative colitis. Full article

Background: Colorectal cancer (CRC) is a complex and increasingly prevalent malignancy with significant challenges in its treatment and prognosis. This study aims to explore the role of the SLC4A4 transporter as a biomarker in CRC progression and its potential as a therapeutic target, particularly in relation to tumor acidity and immune response. Methods: The study utilized computational approaches, including receptor-based virtual screening and high-throughput docking, to identify potential SLC4A4 inhibitors. A model of the human SLC4A4 structure was generated based on CryoEM data (PDB ID 6CAA), and drug candidates from the DrugBank database were evaluated using two computational tools (DrugRep and CB-DOCK2). Results: The study identified the compound (5R)-N-[(1r)-3-(4-hydroxyphenyl)butanoyl]-2-decanamide (DB07991) as the best ligand, demonstrating favorable binding affinity and stability. Molecular dynamics simulations revealed strong protein–ligand interactions with consistent RMSD (~0.25 nm), RMSF (~0.5 nm), compact Rg (4.0–3.9 nm), and stable SASA profiles, indicating that the SLC4A4 structure remains stable upon ligand binding. Conclusions: The findings suggest that DB07991 is a promising drug candidate for further investigation as a therapeutic agent against CRC, particularly for targeting SLC4A4. This study highlights the potential of computational drug repositioning in identifying effective treatments for colorectal cancer. Full article

RAB11, a pivotal RabGTPase, regulates essential cellular processes such as endocytic recycling, exocytosis, and autophagy. The protein was implicated in various human diseases, including cancer, neurodegenerative disorders, viral infections, and podocytopathies. However, a small-molecular inhibitor is lacking. The complexity and workload associated with potential assays make conducting large-scale screening for RAB11 challenging. We employed a tiered approach for drug discovery, utilizing deep learning-based computational screening to preselect compounds targeting a specific pocket of RAB11 protein with experimental validation by an in vitro platform reflecting RAB11 activity through the exocytosis of GFP. Further validation included the exposure of Drosophila by drug feeding. In silico pre-screening identified 94 candidates, of which 9 were confirmed using our in vitro platform for Rab11 activity. Focusing on compounds with high potency, we assessed autophagy, which independently requires RAB11, and validated three of these compounds. We further analyzed the dose–response relationship, observing a biphasic, potentially hormetic effect. Two candidate compounds specifically caused a shift in Rab11 vesicles to the cell periphery, without significant impact on Rab5 or Rab7. Drosophila larvae exposed to another candidate compound with predicted oral bioavailability exhibited minimal toxicity, subcellular dispersal of endogenous Rab11, and a decrease in RAB11-dependent nephrocyte function, further supporting an inhibitory role. Taken together, the combination of computational screening and experimental validation allowed the identification of small molecules that modify the function of Rab11. This discovery may further open avenues for treating RAB11-associated disorders. Full article

Tuberculosis (TB) caused by Mycobacterium tuberculosis is a leading infectious cause of death globally. The treatment of patients becomes much more difficult for the increasingly common multi-drug resistant TB. Topoisomerase I is essential for the viability of M. tuberculosis and has been validated as a new target for the discovery of novel treatment against TB resistant to the currently available drugs. Virtual high-throughput screening based on machine learning was used in this study to identify small molecules that target the binding site of divalent ion near the catalytic tyrosine of M. tuberculosis topoisomerase I. From the virtual screening of more than 2 million commercially available compounds, 96 compounds were selected for testing in topoisomerase I relaxation activity assay. The top hit that has IC₅₀ of 7 µM was further investigated. Commercially available analogs of the top hit were purchased and tested with the in vitro enzyme assay to gain further insights into the molecular scaffold required for topoisomerase inhibition. Results from this project demonstrated that novel small molecule inhibitors of bacterial topoisomerase I can be identified starting with the machine-learning-based virtual screening approach. Full article