Search Results (583)

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by cholinergic dysfunction, amyloid-β aggregation, mitochondrial stress, and aberrant kinase activity. Carotenoids, naturally occurring pigments with antioxidant and neuroprotective properties, have emerged as promising candidates for AD intervention. In this study, we performed a systematic stepwise computational screening of a large carotenoid library (n = 1191) to identify multitarget candidates against AD–related proteins. The workflow consisted of predefined ADMET filtering (oral absorption > 90%, Caco-2 > 0.9, logBB > −1, and absence of major CYP inhibition and toxicity alerts), reducing the dataset to 61 compounds, followed by multi-target molecular docking against AChE, BChE, BACE-1, MAO-B, and GSK3-β. Compounds were ranked using an aggregated mean docking score across all five targets, and the top-performing candidate was subjected to detailed mechanistic analyses. Hopkinsiaxanthin emerged as the highest-ranked multitarget carotenoid and was further evaluated using frontier molecular orbital (FMO) analysis, pharmacophore modeling, 100 ns molecular dynamics (MD) simulations, MM/PBSA binding free energy calculations, and per-residue decomposition. Docking predicted favorable estimated binding affinities toward all targets. MD simulations confirmed stable receptor–ligand complexes with low RMSD values (0.278–0.285 nm). MM/PBSA analysis indicated favorable binding free energies, particularly for GSK3-β (−22.73 kcal/mol) and AChE (−21.50 kcal/mol). Per-residue decomposition identified key hotspot residues driving stabilization. Overall, this structured computational framework identifies Hopkinsiaxanthin as a promising multitarget scaffold and supports its prioritization for experimental validation in AD models. Full article

Pharmacological inhibition of the nucleoside transporter hENT1 is a promising therapeutic target across a range of diseases, including cardiovascular disorders, neurodegenerative conditions, and cancer. However, current inhibitors lack drug-like properties, necessitating the development of new inhibitors with improved pharmacological profiles. We employed a dual-pharmacophore virtual screening protocol to identify putative hENT1 inhibitors from a library of over 2 million compounds, followed by structure-based molecular docking. To validate the inhibition effect of the lead compounds, we established a functional assay using gemcitabine (GEM)-induced cytotoxicity as a readout of hENT transport activity using eight cancer cell lines. H292 was the optimal cancer cell line for the validation assay based on its high GEM sensitivity (IC50 = 28 nM) and the concentration-dependent cytotoxicity inhibition of the reference inhibitor NBTI, a hENT1 inhibitor. Of the 19 candidate compounds, two leads (compounds 2 and 3) demonstrated potency comparable to NBTI, increasing GEM IC50 values by 2.2- and 2.9-fold at 5 µM, respectively. Both compounds were non-cytotoxic to normal fibroblasts, exhibited favorable ADME properties, displayed superior docking scores of −12.63 and −12.49 kcal/mol compared to NBTI (−9.06 kcal/mol), and displayed a novel vertical binding orientation within the hENT1 binding pocket distinct from NBTI’s horizontal mode. This study established a validated non-radioactive, gemcitabine-based functional assay for hENT inhibitor discovery and identified two putative inhibitors with therapeutic potential for cancer chemosensitization, pain management, and cardio- and neuroprotection. The non-radioactive functional assay overcomes the limitations of traditional radiolabeled methods, enabling scalable, broader screening applications. Full article

Background/Objectives: Protein kinases play a crucial role in cancer initiation, progression, and therapeutic resistance by regulating signalling pathways involved in tumour growth and survival. Consequently, they represent major targets in anticancer drug discovery. Among heterocyclic scaffolds explored in kinase inhibitor design, benzimidazole has emerged as a privileged structure due to its strong hydrogen-bonding capability and structural resemblance to purine moieties. Triazole motifs are also widely incorporated into bioactive molecules because of their metabolic stability, favourable electronic properties, and ability to establish key interactions within kinase active sites. This review aims to summarise and critically discuss benzimidazole- and triazole-based kinase inhibitors, both as individual scaffolds and as hybrid systems, with emphasis on their kinase targets and multitarget potential. Methods: The relevant literature was surveyed from major scientific databases focusing on studies describing the synthesis, biological evaluation, and molecular modelling of benzimidazole- and triazole-containing kinase inhibitors. Results: Numerous studies demonstrate that both benzimidazole and triazole scaffolds exhibit significant kinase inhibitory activity against oncogenic targets, including EGFR, cyclin-dependent kinases (CDKs), and components of the PI3K/Akt/mTOR signalling pathway. Hybrid molecules combining these pharmacophores frequently enhance binding interactions and facilitate the development of multitarget kinase inhibitors. Structure–activity relationship trends indicate that pharmacophore accessibility, substitution patterns, and linker architecture influence inhibitory potency and selectivity. Conclusions: Overall, benzimidazole- and triazole-based scaffolds represent promising platforms for developing next-generation multitarget anticancer agents and provide valuable insights for the rational design of improved kinase inhibitors. Full article

Protein kinase inhibition can be achieved through various mechanisms, including blocking phosphorylation activity or disrupting regulatory interactions. While small molecule inhibitors have shown promise, their selectivity remains challenging due to the structural similarities among kinase catalytic sites. To design selective kinase inhibitors based on peptide terminal tail interactions with the activation segment, focusing on five kinases with different conformational states: GSK3, PAK4, TTN (OUT conformation) and PKB, FLT3 (IN conformation). Three-dimensional structures from RCSB PDB were optimized using MODELLER version 9.0. Peptide sequences were designed with PeptiDerive (Rosetta) and RosettaDesign version 3.5, followed by pharmacophore modeling based on key interaction residues. Virtual screening was then conducted with PyRx 0.8 and molecular docking with AutoDock Vina 1.1.2. Molecular dynamics simulations were performed using Desmond v6.6 (Schrödinger Suite 2016, Multisim v3.8.5.19) (100 ns, NPT ensemble, 300 K). Analysis of the five kinases revealed distinct interaction profiles with designed peptidomimetic compounds. Kinases displaying the IN conformation of the activation segment (PKB and FLT3) consistently showed superior stability and stronger interaction profiles compared to those in the OUT conformation. The designed compounds formed key hydrogen bonds and hydrophobic interactions with critical residues in the activation segment binding pocket. The most promising inhibitors demonstrated stability throughout the molecular dynamics simulations, with IN conformation kinases maintaining more consistent conformational profiles than their OUT conformation counterparts. Kinases with IN conformation of the activation segment demonstrated superior stability and interaction profiles compared to OUT conformations. These findings contribute to our understanding of selective kinase inhibition and provide a framework for developing novel inhibitors, particularly for PKB and FLT3. The implications of this study extend to rational drug design approaches that leverage natural regulatory mechanisms for therapeutic intervention, though further optimization is needed for GSK-3β, PAK4, and TTN to improve stability and binding affinity. Full article

Organophosphorus (OP) nerve agents inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) by phosphylating the catalytic serine. Oxime reactivators can restore enzymatic activity by a nucleophilic attack of the oximate anion on the phosphorus center of the enzyme–OP conjugate; however, clinically used oximes show agent- and enzyme-dependent performance and are particularly challenged by A-series compounds. Here, an in silico strategy is presented to identify candidate antidotes for OP poisoning, including A-series agents. Pharmacophore models are generated from benchmark/template oximes. Pharmacophore-based virtual screening is used to retrieve hit-like scaffolds from the available chemical space, after which selected hits are converted into oxime analogs. Template oximes and newly designed oximes are then docked into the active site of AChE or BChE inhibited by specific nerve agents. The predicted reactivation potential is assessed using mechanistically motivated geometric criteria derived from the accepted reactivation hypothesis, including the distance between the oximate oxygen and the phosphyl phosphorus and the attack angle, relative to the catalytic serine Oγ. This workflow enables a controlled, pairwise comparison of new oximes against their corresponding template oximes for each enzyme–agent combination, providing a rational basis for prioritizing candidates for synthesis and experimental validation. Using the described workflow, we identified a hit compound with the potential to act as an antidote against A-series nerve agent A-230 poisoning. Full article

The berberine derivative 13-(2-methylbenzyl)-berberine (BED) has been shown to inhibit the MexXY-OprM efflux system of Pseudomonas aeruginosa (PA), a key contributor to aminoglycoside resistance, by interacting with the inner membrane protein MexY at an allosteric pocket (ALP). To enhance binding efficacy, this study aims to identify novel chemical scaffolds that target the MexY allosteric pocket through an integrated computational strategy. In this work, a ligand-based virtual screening (LBVS) approach was employed using a 2D/3D pharmacophore model derived from BED to perform in silico screening of an Enamine compound library, which encompasses a broad and diverse chemical space. A key objective was to compare the predictive performance of this pharmacophore-based workflow with a structure-based (SB) strategy incorporating molecular docking and molecular dynamics (MD) simulations. Notably, the top-ranked LBVS hits were consistently validated by docking and MD analyses, showing stable binding and interaction patterns comparable or superior to those of BED. This convergence between ligand-based (LB) and SB methods highlights the internal coherence of the workflow and supports the robustness of the pharmacophore hypothesis. The identified scaffolds generally displayed high hydrophobicity, consistent with the physicochemical nature of the binding site, but resulting in limited aqueous solubility and complicating their experimental evaluation. While these features confirm the importance of hydrophobic interactions in MexY recognition, with a particular focus on some few residues, such as Phe560, it also underscores the need for formulation strategies or rational scaffold modifications introducing moderate polarity without weakening key contacts. Overall, the integrated computational strategy not only yields promising lead chemical structures but also provides a solid basis for their future optimization, ultimately supporting the design of new efflux pump inhibitors (EPIs) capable of contributing to improved antibiotic susceptibility in multidrug-resistant PA strains. Full article

The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q² = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R² = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. Full article

Background: There is currently increasing interest in atypical opioid compounds capable of expanding their clinical applications beyond pain management, including the treatment of psychiatric disorders and substance abuse. In this context, the cyclotetrapeptide c[Trp-Phe-D-Pro-Phe] (CJ-15,208, 1) and its derivatives represent an unusual class of opioid peptides. This compound was found to be a mixed KOR/MOR antagonist in vitro, but it acted as an agonist in vivo. For its diverse analogues, it appeared that receptors’ affinity, selectivity, and agonist/antagonist activity greatly varied upon modifications to backbone geometry and the 3D display of pharmacophores. Methods: We utilized NMR, molecular dynamics, and molecular docking to analyze 3D structures and pharmacodynamic properties of selected representative cyclopeptide analogues of 1. Results: The simulations support that, despite its contradictory functional activity in vitro and in vivo, 1 can bind to the active conformation of receptors in an agonist-like fashion. In general, Trp appeared to be the fundamental pharmacophore in the ligand–receptor complexes. In particular, agonists showed a direct interaction between the indole ring and the carboxylate of the conserved Asp(3:32). Conclusions: These studies support a distinctive pharmacodynamic model for this class of compounds, potentially useful for the design of opioid compounds with novel binding/activity profiles and improved therapeutic effects. Full article

Background: The growing recognition of shared molecular pathways and molecular signatures between cardiovascular diseases and cancer has motivated interest in exploring antihypertensive-associated chemical space for oncological applications. Concurrently, artificial intelligence (AI)-driven molecular generation has enabled the rapid creation of virtual lead candidates for specific therapeutic indications, although their broader biological interaction profiles often remain unexplored. Methods: In this paper, we explore the computational screening of a library of AI-generated antihypertensive virtual lead compounds to evaluate their polypharmacological anticancer potential. The compounds were originally designed and prioritized for modulating $\mathsf{\beta }$-adrenergic receptors but are here re-evaluated in a cancer-focused context using a multi-stage in silico approach. We chose five (5) known cancer target proteins and performed compound profiling for drug-likeness, pharmacokinetic suitability, and safety. Docking simulations, binding free energy estimates, molecular interaction mapping, and pharmacophore modeling were used to evaluate the molecules’ interactions with the cancer-linked protein targets. We employed the binding free energy estimates of the ligand–protein complexes to determine compounds with polypharmacological anticancer potential. In addition, molecular dynamics simulations of some of the compounds with polypharmacological anticancer potential were employed to evaluate binding stability and dynamic behavior of selected ligand–target complexes. Results: Several compounds showed good docking scores, physicochemical characteristics, and pharmacokinetic profiles. Also, the results reveal that several AI-generated antihypertensive virtual leads exhibit favorable multi-target binding profiles, with consistent docking affinities and stable interaction networks across multiple cancer-related targets. Conclusions: Our findings suggest that several of the hypothetically evaluated compounds exhibit favorable physicochemical properties, acceptable predicted pharmacokinetic and safety profiles, and consistent predicted binding affinities across multiple cancer-relevant targets. Full article

Arachidonic acid 5-lipoxygenase (ALOX5), an enzyme critical for lipid mediator synthesis, demonstrates significant upregulation in clinically distinct disease states. Current research identifies its aberrant activity in neurodegenerative pathologies (e.g., Parkinson’s disease), solid tumors, hematological cancers, metabolic dysregulation linked to diabetic nephropathy, and vascular remodeling in hypertension and coronary artery disease. These findings collectively implicate ALOX5 as a multifunctional driver of chronic inflammation and tissue damage across organ systems. Despite the significant clinical significance of ALOX5, developing effective inhibitors for this target remains challenging, with most candidates still undergoing clinical evaluation. This study employs a multi-stage computational approach to identify novel ALOX5 inhibitors with strong drug-like properties. By compiling compounds with documented ALOX5 inhibitory activity and IC50 values from PubChem, ChEMBL, and MedChemExpress databases, we established a ligand-based pharmacophore model to virtually screen terpenoid derivatives. The selection of terpenoid compounds for virtual screening is primarily due to their dual role as natural products exhibiting significant structural diversity alongside a broad spectrum of known biological activities. This provides an ideal starting point for the efficient discovery of structurally novel lead compounds with drug potential, while also being well-suited for structure-based computational evaluation. Two lead compounds (29835 and 38032) were identified through ADMET property prediction and scaffold modification-guided optimization. Molecular docking analysis revealed superior binding affinities for these candidates (−8.31 and −10.26 kcal/mol, respectively) compared to Zileuton (−7.39 kcal/mol), indicating stable and favorable interactions within the target protein’s active site. The binding stability of these complexes was further confirmed by 100 ns molecular dynamics simulations, which demonstrated sustained structural integrity of the protein–ligand systems. Collectively, computational findings suggest these compounds as promising ALOX5 inhibitors. However, given the theoretical framework of this work, subsequent experimental validation via in vitro and in vivo pharmacological assays is imperative to verify their therapeutic potential. Full article

Triterpenoids are a diverse class of naturally occurring compounds with a wide range of pharmacological properties, including anticancer, anti-inflammatory, antimicrobial, and antiviral activities. Among them, ursolic acid (UA), oleanolic acid (OA), and betulinic acid (BA) have emerged as key scaffolds due to their broad therapeutic potential and structural versatility. However, the clinical application of these compounds is often limited by issues such as poor solubility, bioavailability, and selectivity. To address these challenges, research conducted between 2015 and 2025 increasingly focused on the development of triterpenoid-based hybrid molecules, in which the triterpenoid scaffolds are chemically linked to other bioactive pharmacophores. This approach aims to enhance therapeutic efficacy through synergistic action, improved pharmacokinetics, and multitarget interactions. This comprehensive review explores recent advancements in the design, synthesis, and evaluation of hybrid derivatives of selected triterpenoids, particularly UA, OA, and BA. Emphasis is placed on the structure–activity relationships (SARs), biological evaluations, and mechanisms of action of these hybrid compounds across various disease models. The review also highlights current challenges, research gaps, and future perspectives in the rational development of triterpenoid-based hybrids as potential leading candidates in drug discovery. Full article

Cyclooxygenase-2 (COX-2) is a key enzyme in inflammatory pathways and serves as a therapeutic target in the treatment of inflammation-related diseases. Curcumin, a bioactive polyphenol from turmeric, has gained scientific attention due to its potent anti-inflammatory properties, largely mediated through COX-2 inhibition. However, the poor solubility and limited bioavailability of Curcumin limit its potential as a therapeutic agent targeting inflammatory diseases. We used an in silico approach to identify Curcumin-like scaffolds as novel COX-2 inhibitors with improved drug-like properties and therapeutic potential. A pharmacophore model derived from the key binding moieties of Curcumin was used to virtually screen the ZINC-22 database, identifying 237 candidate compounds for further evaluation. Molecular docking further prioritized these compounds to 10 candidates with the highest binding affinities. Most hits obeyed Lipinski’s rules, except for ZINC32605424 and ZINC47133707, which exhibited high LogP and molecular weight, respectively. Toxicity screening indicated that ZINC47133693 and ZINC09499196 exhibited high safety profiles, with ZINC15942488 being highly toxic. Furthermore, certain hits such as ZINC32605424 and ZINC15942488 were predicted to be P-glycoprotein substrates and potential inhibitors of cytochrome P450. Molecular dynamics simulations confirmed the stability of COX-2–ligand complexes, with critical interactions observed at conserved residues Tyr323 and Leu320. Binding energy calculations identified ZINC32605424 as the strongest COX-2 binder, mainly stabilized by Van der Waals forces. Overall, compounds such as ZINC32605424, ZINC08644750, ZINC47133693, and ZINC09499196 demonstrated potent COX-2 inhibition. These candidates show strong potential for further preclinical validation in studies investigating inflammation-related metabolic complications. Full article

This study details the synthesis, characterization, molecular docking and preliminary biological evaluation of a new heterocyclic compound, 2-((4-morpholino-1,2,5-thiadiazol-3-yl)oxy)benzaldehyde. This molecule was designed using an artificial intelligence (AI)-based molecular generative model. It was synthesized through a nucleophilic substitution between 3-chloro-4-morpholino-1,2,5-thiadiazole and 2-hydroxybenzaldehyde. Structural elucidation was performed using ¹H NMR, ¹³C NMR, Elemental Analysis, and Single Crystal X-ray diffraction. AI-guided in silico predictions suggested promising pharmacophoric features and potential biological activity. Preliminary biological evaluation, primarily through anticancer assays, demonstrated moderate to significant activity, supporting further investigation. The findings therefore suggest that this AI-generated molecule could serve as a lead scaffold for developing drugs targeting cancer and other infectious diseases. Full article

Pseudomonas aeruginosa is a Gram-negative pathogen with a remarkable capacity to acquire multiple resistance mechanisms, severely limiting current therapeutic options. Consequently, the identification of new antimicrobial agents remains a critical priority. In this study, an integrated in silico-guided strategy was applied to identify small molecules with antibacterial potential against P. aeruginosa, targeting the quorum-sensing regulator LasR (PDB ID: 2UV0) and elastase (PDB ID: 1U4G). Pharmacophore modeling was performed for both targets, followed by ligand-based virtual screening, structure-based virtual screening (SBVS), and MM-GBSA (Molecular Mechanics-Generalized Born Surface Area) binding free energy calculations. Top-ranked compounds based on predicted binding affinity were selected for in vitro cytotoxicity and antibacterial evaluation. Antimicrobial activity was assessed against three P. aeruginosa strains: an American Type Culture Collection (ATCC) reference strain, a clinically susceptible isolate, and an extensively drug-resistant (XDR) clinical isolate. SBVS yielded docking scores ranging from −6.96 to −12.256 kcal/mol, with MM-GBSA binding free energies between −18.554 and −88.00 kcal/mol. Minimum inhibitory concentration (MIC) assays revealed that MolPort-001-974-907, MolPort-002-099-073, MolPort-008-336-135, and MolPort-008-339-179 exhibited MIC values of 62.5 µg/mL against the ATCC strain, indicating weak-to-moderate antibacterial activity consistent with early-stage hit compounds. MolPort-008-336-135 showed the most favorable activity against the clinically susceptible isolate, with an MIC of 62.5 µg/mL, while maintaining HepG2 cell viability above 70% at this concentration and an half-maximal inhibitory concentration (IC₅₀) greater than 500 µg/mL. In contrast, all tested compounds displayed MIC values above 62.5 µg/mL against the XDR isolate, reflecting limited efficacy against highly resistant strains. Overall, these results demonstrate the utility of in silico-driven approaches for the identification of antibacterial hit compounds targeting LasR and elastase, while highlighting the need for structure–activity relationship optimization to improve potency, selectivity, and activity against multidrug-resistant P. aeruginosa. Full article

Accurately identifying drug off-targets is essential for reducing toxicity and improving the success rate of pharmaceutical discovery pipelines. However, current deep learning approaches often struggle to fuse chemical structure, protein biology, and multi-target context. Here, we introduce HDPC-LGT (Hybrid Dual-Prompt Cross-Attention Ligand–Protein Graph Transformer), a framework designed to predict ligand binding across sixteen human translation-related proteins clinically associated with antibiotic toxicity. HDPC-LGT combines graph-based chemical reasoning with protein language model embeddings and structural priors to capture biologically meaningful ligand–protein interactions. The model was trained on 216,482 experimentally validated ligand–protein pairs from the Chemical Database of Bioactive Molecules (ChEMBL) and the Protein–Ligand Binding Database (BindingDB) and evaluated using scaffold-level, protein-level, and combined holdout strategies. HDPC-LGT achieves a macro receiver operating characteristic–area under the curve (macro ROC–AUC) of 0.996 and a micro F1-score (micro F1) of 0.989, outperforming Deep Drug–Target Affinity Model (DeepDTA), Graph-based Drug–Target Affinity Model (GraphDTA), Molecule–Protein Interaction Transformer (MolTrans), Cross-Attention Transformer for Drug–Target Interaction (CAT–DTI), and Heterogeneous Graph Transformer for Drug–Target Affinity (HGT–DTA) by 3–7%. External validation using the Papyrus universal bioactivity resource (Papyrus), the Protein Data Bank binding subset (PDBbind), and the benchmark Yamanishi dataset confirms strong generalisation to unseen chemotypes and proteins. HDPC-LGT also provides biologically interpretable outputs: cross-attention maps, Integrated Gradients (IG), and Gradient-weighted Class Activation Mapping (Grad-CAM) highlight catalytic residues in aminoacyl-tRNA synthetases (aaRSs), ribosomal tunnel regions, and pharmacophoric interaction patterns, aligning with known biochemical mechanisms. By integrating multimodal biochemical information with deep learning, HDPC-LGT offers a practical tool for off-target toxicity prediction, structure-based lead optimisation, and polypharmacology research, with potential applications in antibiotic development, safety profiling, and rational compound redesign. Full article