Search Results (375)

Chagas disease affects millions of people worldwide, including those in Latin America. The only drugs available for its treatment are benznidazole and nifurtimox. However, these drugs present high toxicity and limited efficacy. Therefore, the search for new treatments continues. In this regard, computer-assisted drug design has been implemented in scientific research for drug repurposing, allowing for reduced costs and time. Therefore, the objective of this work was to search for analogs of FDA-approved drugs with activity against Trypanosoma cruzi through ligand-based virtual screening and their biological evaluation against blood trypomastigotes. The compound TD-095 (LC₅₀ = 48.60 and 13.75 µM), a ketanserin analogue, TS-936 (LC₅₀ = 71.55 and 37.54 µM), a terfenadine analogue, and TD-831 (LC₅₀ = 75.94 and 26.17 µM), a sulfasalazine analogue, were considered as potential trans-sialidase inhibitors; TIM-967 (LC₅₀ = 69.70 and 39.69 µM) and LK-284 (LC₅₀ = 116.7 and 82.29 µM), two sulfonylurea analogues, were considered as potential triosephosphate isomerase inhibitors, showing better trypanocidal activity against NINOA and INC-5 strains, respectively, than the reference drugs. Molecular dynamics simulations predicted the stability of the compounds in complex with their respective proteins. Finally, the ADMET predictive analysis showed favorable properties for the compounds. These results support continued research into new agents against Trypanosoma cruzi, using structures of drugs already approved by the FDA. Full article

Molecular docking is a foundational technique in computational drug discovery, widely used to generate binding hypotheses, prioritize compounds, and support target-selectivity studies. The continued growth of open-source docking resources, together with improvements in scoring functions, sampling strategies, and hardware acceleration, has substantially lowered barriers to teaching, early-stage hit identification, and reproducible research. Beyond standalone docking engines, the open-source ecosystem now encompasses browser-accessible tools, preparation and analysis utilities, integrative modeling platforms, and AI-augmented methods for pose prediction, rescoring, and virtual screening. These developments have made docking workflows more accessible, customizable, and transparent across diverse research settings. This review examines open-source docking from a workflow-centered perspective, spanning study design, structural-data acquisition, binding-site definition, receptor and ligand preparation, docking execution, and post-docking validation. It further evaluates how open AI methods are being incorporated into these stages to expand structural coverage, improve screening efficiency, and support contemporary structure-based drug design. Collectively, this review outlines a practical and evidence-based framework for the effective use of open-source docking and virtual-screening pipelines in modern drug discovery. Full article

Monkeypox virus (MPXV) is emerging as a global public health concern due to its nature of spread. There are limited treatment options, as the sole drug for treatment is lacking, highlighting the need for new therapeutic options. The use of computer-aided drugs discovery such as molecular docking, molecular dynamic (MD) simulations and post-simulation analysis are important tools in identifying potential compounds that can target specific proteins of the virus, such as DNA polymerase to stop virus replication. This study employed molecular docking and molecular simulation with the aim to identify potential inhibitors for MPXV treatment from the ZINC Database. Molecular docking was performed using PyRx 0.8 version after virtual screening of the ZINC database using the Tranches tool; then, toxicity prediction of the selected compounds was performed using the ProTox-3.0 web server. Molecular dynamics simulation was conducted using GROMACS version 4.5 to evaluate the structural stability and dynamic behavior of the protein–ligand complex for the best interacting compound. Furthermore, post-simulation analysis was conducted using standard GROMACS utilities for visualizing time-dependent properties from MD simulations. A total of 16 compounds were shortlisted based on their molecular docking scores and interaction profiles with the monkeypox virus DNA polymerase (PDB ID: 8HG1). The leading compound, ZINC000019418450, demonstrated strong binding affinity (−7.4 kcal/mol). According to post-simulation analysis, all top compounds formed between one and five hydrogen bonds and up to eleven hydrophobic contacts with residues within the active site, thus providing strong geometric and energetic evidence for binding stability. Notably, our identification of ZINC000104288636 as a Class 6 compound with an LD₅₀ of 23,000 mg/kg adds translational value by highlighting candidates with low predicted acute toxicity. Overall, this study lays a solid foundation for the rational design of next-generation monkeypox antiviral therapeutics. Future work is needed for experimental validation of prioritized compounds to assess their biochemical efficacy and pharmacological potential. Full article

Ligand-based virtual screening (LBVS) seeks strong early enrichment when searching ultra-large libraries, but practical screening often relies on 1D/2D descriptions while 3D information is expensive and uncertain due to conformer generation and alignment. We propose GNN-MA, a retrieval-style pairwise scoring model for query–candidate molecular pairs that uses molecular graphs as a unified representation. Built on intra-graph message passing, GNN-MA adds cross-graph attention to learn atom-level soft alignment that focuses on key substructures relevant to activity matching, and introduces a bond-to-atom semantic aggregation module to better exploit chemical bond cues for similarity scoring. The framework uses 2D molecular graphs derived from SMILES for retrieval-style matching and does not rely on explicit 3D conformational modeling or alignment. Experiments on DUD-E and LIT-PCBA show that GNN-MA achieves competitive overall discrimination (ROC-AUC) and, relative to its ablated variants, provides consistent gains in early-enrichment metrics (EF@1–5%) on DUD-E, while on LIT-PCBA the improvements are more target-dependent. The learned atom-level soft alignment also provides a qualitative interpretability cue in case studies. Throughput benchmarks suggest that GNN-MA is most suitable as a re-ranking/refinement model after a fast prefiltering stage. 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

Respiratory syncytial virus (RSV) poses a substantial global health burden, particularly in infants and the elderly. The fusion (F) protein is a key therapeutic target for inhibiting RSV entry. In this study, we performed a structure-based virtual screening of the Comprehensive Marine Natural Products Database (CMNPD) to discover novel anti-RSV agents targeting the prefusion F protein trimer. Screening of 31,561 compounds via molecular docking, followed by stringent ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and MM/GBSA (Molecular Mechanics/Generalized Born Surface Area) binding free energy calculations, identified 11 promising candidates. Among these, manzamine alkaloids exhibited the most favorable docking scores (as low as −13.3 kcal/mol) and promising Ligand Efficiency (LE) values. These molecules primarily interact with conserved hydrophobic residues (Phe140, Phe488) through hydrophobic interactions, π-stacking, and electrostatic forces. Our study highlights marine natural products, especially manzamine alkaloids, as promising leads for the development of novel, orally bioavailable RSV fusion inhibitors, potentially offering avenues to overcome existing drug resistance. However, these computational findings require in vitro validation to confirm efficacy. Full article

Malaria is a major global health concern caused by Plasmodium parasites, among which Plasmodium falciparum is responsible for the most severe and fatal cases. The emergence of drug resistance to existing antimalarial therapies necessitates the discovery of novel molecular targets and chemically distinct inhibitors. Current study employed an integrated in silico drug discovery pipeline combining high-throughput structure-based virtual screening of 1549 deep-sea marine microbial metabolites with MM-GBSA binding free-energy estimation, QikProp-based ADME/Tox profiling, and 100 ns molecular dynamics (MD) simulations to link rapid screening with dynamic verification of binding stability. Molecular docking against Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH; PDB ID: 7KZ4) yielded five top-ranked compounds with Glide scores ranging from −12.02 to −10.61 kcal·mol⁻¹, which is higher than the Primaquine (−6.920 kcal·mol⁻¹; a clinically approved antimalarial reference compound). MM-GBSA analysis further refined hit selection, producing binding free energies (ΔG_bind) between −63.28 and −31.37 kcal·mol⁻¹. The selected lead compounds included (±)-puniceusine P, aspergilol F, tersaphilone C, 4-carbglyceryl-3,3′-dihydroxy-5,5′-dimethyldiphenyl ether, and 15-O-methyl ML-236A. The top hits were subjected to 100 ns MD simulations in Desmond, demonstrating stable protein–ligand complexes, particularly for (±)-puniceusine P and 15-O-methyl ML-236A (protein backbone root mean square deviation (RMSD; ~0.8–1.0 Å). ADME profiling indicated acceptable predicted physicochemical and pharmacokinetic properties. Overall, these in silico findings highlight deep-sea marine microbial metabolites as promising PfDHODH inhibitor candidates requiring experimental validation. Full article

Background: Chagas disease is a major public health problem, especially in Latin American countries, and benznidazole and nifurtimox are currently the only drugs available for its treatment. However, they present several disadvantages, such as low availability, high toxicity, and limited efficacy, which often result in treatment discontinuation. In recent decades, bioinformatics studies have accelerated the field of drug repurposing, reducing time and costs. In this study, the aim was to identify novel cruzain inhibitors from the analogs of FDA-approved drugs with trypanocidal activity. Methods: A ligand-based virtual screen, along with molecular docking analysis, was carried out, and the selected compounds were evaluated for their trypanocidal activity against trypomastigotes of two endemic Mexican strains and their inhibitory activity on cysteine proteases. Results: A cefsulodin analog (LC₅₀ = 126.18 and 77.50 µM), two flucloxacillin analogs (LC₅₀ = 94.05 and 101.73 µM; 48.74 and 64.49 µM), and one piperacillin analog (LC₅₀ = 48.46 and 83.68 µM) had better trypanocidal activity and selectivity index against the NINOA and INC-5 strains than the reference drugs. Enzymatic evaluation showed that all four compounds inhibited cysteine proteases (IC₅₀ < 840.03 µM). Furthermore, molecular dynamics simulations predicted the stability of the compound–protein complex, while the docking test on human cathepsin L predicted their potential selectivity. Finally, our in silico analysis of ADMET properties showed that all compounds exhibited favorable profiles. Conclusions: These results encourage the development of new and more potent anti-Trypanosoma cruzi agents using FDA-approved drugs as scaffolds. 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

Bromodomain-containing protein 9 (BRD9) belongs to the non-canonical BAF chromatin remodeling complex and represents a relevant therapeutic target in pathologies featuring dysregulated epigenetic control. The absence of clinically validated inhibitors and the need for diversified chemical entities highlight the interest in identifying new scaffolds targeting this protein. In this study, Saturation Transfer Difference Nuclear Magnetic Resonance (STD NMR) was employed to assess its suitability for characterizing BRD9–ligand interactions within a fragment-based discovery framework. STD NMR conditions were first optimized using the known BRD9 ligand 1, verifying the presence of interaction signals. A pharmacophore-based virtual screening campaign was then performed using libraries of commercially available fragments, leading to the selection of a novel isatin derivative, i.e., compound 2, whose binding was demonstrated in AlphaScreen assays. STD NMR experiments provided epitope mapping consistent with the predicted binding mode, thus supporting the stability of the interaction in solution. Moreover, a competitive STD experiment demonstrated displacement of 2 by a reference ligand, confirming the binding within the canonical BRD9 pocket. Overall, this study establishes STD NMR as a reliable approach for probing BRD9–ligand interactions and for the identification and validation of BRD9-targeting scaffolds suitable for future structure-guided optimization. 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

Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article

Background/Objectives: Diabetes mellitus is a serious global disease characterized by chronic hyperglycemia, resulting from defects in insulin secretion, insulin action, or both. It represents a major health concern affecting millions of people worldwide. This condition can lead to severe complications significantly affecting patients’ quality of life. Due to the limitations and side effects of current therapies, the search for safer and more effective antidiabetic agents, particularly from natural sources, has gained considerable attention. This study investigates the antidiabetic potential of seaweed-derived compounds through structure-based virtual screening targeting α-glucosidase. Methods: A library of compounds derived from the Seaweed Metabolite Database was subjected to a hierarchical molecular docking protocol against α-glucosidase. Extra Precision (XP) docking was employed to identify the top-ranked ligands based on their binding affinities. Drug-likeness was assessed according to Lipinski’s Rule of Five, followed by pharmacokinetic and toxicity predictions to evaluate ADMET properties. Density Functional Theory (DFT) calculations were performed to analyze the electronic properties and chemical reactivity of the selected compounds. Furthermore, molecular dynamics simulations were carried out to examine the stability and dynamic behavior of the ligand–enzyme complexes. Results: Following XP docking and ADMET prediction, four promising compounds were selected: Colensolide A, Rhodomelol, Callophycin A, and 7-(2,3-dibromo-4,5-dihydroxybenzyl)-3,7-dihydro-1H-purine-2,6-dione. Molecular dynamics simulations further confirmed the structural stability and strong binding interactions of these compounds within the α-glucosidase active site. Conclusions: This investigation demonstrated the important role of seaweed-derived compounds in inhibiting α-glucosidase activity. Further experimental validation is warranted to confirm their biological activity and therapeutic potential. Full article

Serotonin receptors, in particular 5-HT_1A and 5-HT_2A receptors, are important molecular targets for the central nervous system (CNS) disorders, such as schizophrenia, depression, anxiety disorders, memory deficits, and many others. Here, we present structural and pharmacological evaluation of a serotonin receptor ligand, SERAAK2, identified in a structure-based virtual screening campaign. Molecular docking studies revealed that SERAAK2 binds with its molecular targets via Asp3.32 as the main anchoring point, which is typical for orthosteric ligands of aminergic GPCRs. Molecular dynamics simulations confirmed the stability of the ligand binding poses in the studied receptors. MMGBSA calculations were in accordance with the receptor in vitro binding affinity studies, which indicated that SERAAK2 is a potent ligand of 5-HT_1A and 5-HT_2A receptors. It was also found that SERAAK2 displays favorable ADMET parameters. The demonstrated anxiolytic- and antidepressant-like effects of SERAAK2 in animal models, which may involve its interaction with 5-HT_1A receptors, warrant further studies to confirm these activities and elucidate the underlying mechanisms. Full article

Aberrant activation of fibroblast growth factor receptor 1 (FGFR1) drives tumor progression in multiple cancer types, yet existing FGFR1 inhibitors suffer from suboptimal target selectivity and dose-limiting toxicities. This study describes an integrated computational approach for the identification of novel FGFR1 inhibitors. We established a computational pipeline incorporating ligand-based pharmacophore modeling, multi-tiered virtual screening with hierarchical docking (HTVS/SP/XP), and MM-GBSA binding energy calculations to evaluate interactions within the FGFR1 kinase domain. From an initial library of 9019 anticancer compounds, three hit compounds exhibited superior FGFR1 binding affinity compared to the reference ligand 4UT801. Scaffold hopping was performed to generate 5355 structural derivatives, among which candidate compounds 20357a–20357c showed improved bioavailability and reduced toxicity as predicted by absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling. Molecular dynamics (MD) simulations validated stable binding modes and favorable interaction energies for these candidates. Collectively, our study identifies structurally novel FGFR1 inhibitors with optimized pharmacodynamic and safety profiles, thereby advancing targeted anticancer drug discovery. Full article