Search Results (241)

Compound repurposing is an efficient method to save both time and costs by redirecting previously synthesized small molecules towards new biological targets. In this research, we employ computational methodologies to investigate and assess target engagement of small molecules as tyrosine kinase inhibitors (TKIs). Therefore, compounds TKI.2a, TKI.2b, TKI.6, TKI.16, TKI.19, and TKI.21b identified from our earlier research, undergo assessments of molecular similarity, docking studies, and pharmacophore modeling along with those discovered through database searches. Compounds TKI.2a, TKI.2b, TKI.6, and TKI.19 appear to exhibit multi-target tyrosine kinase inhibitory activities against VEGFR-2 (Vascular Endothelial Growth Factor Receptor), RET (proto-oncogene tyrosine–protein kinase receptor), PDGFRα (Platelet-Derived Growth Factor Receptor alpha), EGFR (Epidermal Growth Factor Receptor), and HER2 (Human Epidermal Receptor) receptors. Pharmacophore models were applied for ligand-based virtual screening using defined parameters to discover candidate compounds that exhibit drug-likeness with FDA (Food and Drug Administration)-approved tyrosine kinase inhibitors. Molecular docking studies identified lead compounds for each biological target based on their overall affinity values and established interactions. Compound ChEMBL2170947 was found to be the most promising candidate for the VEGFR-2 receptor, ChEMBL5019511 for PDGFRα, ChEMBL2216869 for EGFR, and ChEMBL3355044 for HER2. Full article

Computational chemistry has played a central role in early-stage drug discovery by accelerating target selection, hit identification, and lead optimization. This review summarizes recent developments in molecular docking, pharmacophore modeling, molecular dynamics (MD), and virtual screening (VS), with a focus on their application in practical drug discovery workflows. Advances in docking protocols, including consensus scoring, physics-based rescoring, and ensemble approaches, addressed the challenges of receptor flexibility. Both ligand-based and structure-based pharmacophore models facilitated scaffold hopping and guided library prioritization. MD simulations were used to assess binding pose stability, identify cryptic binding pockets, and characterize solvent interactions. These simulations also supported free-energy calculations using endpoint and alchemical methods. Large-scale VS campaigns employed curated compound libraries, often composed of make-on-demand molecules, and relied on high-performance computing or cloud infrastructure to screen up to 10⁹ compounds. Hits were validated using orthogonal biophysical assays and filtered by absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions. Integrated pipelines combining pharmacophore modeling, docking, MD, and free-energy calculations improved enrichment rates and reduced the number of compounds requiring synthesis. Several case studies demonstrated the identification of nanomolar-affinity leads from ultra-large screening campaigns. The review also addressed ongoing challenges, such as inconsistent scoring of binding affinity, protonation, and tautomeric errors, dataset bias, and reproducibility issues. Strategies to mitigate these limitations included standardized library preparation, adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, and the use of prospective benchmarking protocols. The review discussed emerging trends, including the use of quantum chemistry for electronic structure refinement, ensemble docking guided by cryo-electron microscopy (cryo-EM) data, and the integration of computational tools with automated synthesis and high-throughput screening in closed-loop discovery systems. These approaches have the potential to accelerate the design–make–test cycle, increase hit novelty, and improve decision-making in early drug development programs. 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

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

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

Resistance to imatinib remains a therapeutic challenge, largely driven by point mutations within the kinase domain of the BCR-ABL, among which the T315I substitution constitutes the most clinically significant barrier. Ponatinib effectively inhibits this mutant form but is limited by dose-dependent cardiovascular toxicity, prompting efforts to develop safer and more selective agents. Recent advances highlight aminopyrimidine-derived scaffolds and their evolution into thienopyrimidines, oxadiazoles, and pyrazines with improved activity against BCR-ABL^T315I. Further progress has been achieved with benzothiazole–picolinamide hybrids incorporating a urea-based pharmacophore, which benefit from strategic hinge-region substitutions and phenyl linkers that enhance potency. Parallel research into dual-mechanism inhibitors, including Aurora and p38 kinase modulators, demonstrates additional opportunities for overcoming resistance. Combination strategies, such as vorinostat with ponatinib, provide complementary therapeutic avenues. Natural-product-inspired approaches utilizing fungal metabolites provided structurally diverse scaffolds that could engage sterically constrained mutant kinases. Hybrid molecules derived from approved TKIs, including GNF-7, olverembatinib, and HG-7-85-01, exemplify rational design trends that balance efficacy with improved safety. Molecular modeling continues to deepen understanding of ligand engagement within the T315I-mutated active site, supporting the development of next-generation inhibitors. In this review, we summarized recent progress in the design, optimization, and biological evaluation of small molecules targeting the BCR-ABL^T315I mutation. Full article

Structural insights into methyl-coenzyme M reductase from Methanobrevibacter ruminantium (M. ruminantium) has implications for methane mitigation strategies. Methanogenesis in ruminants is a major contributor to global greenhouse gas emissions, primarily driven by the rumen archaeon M. ruminantium. Central to this process is methyl-coenzyme M reductase (Mcr), an enzyme that catalyzes the final step of methane production. Despite its significance as a chemogenetic target for methane mitigation, the high-resolution structure of M. ruminantium Mcr has remained elusive. Here, we employed homology modeling and CDOCKER simulations within the CHARMM force field to elucidate the structural and functional features of the M. ruminantium Mcr/ligand complexes. We characterized two distinct states: the reduced Mcr_oxi-silent state bound to HS-CoM and CoB-SH, and the oxidized Mcr_silent state bound to the heterodisulfide CoM-S-S-CoB. Alanine-scanning mutagenesis identified 71 and 62 key residues per active site for each state, respectively, revealing the fundamental determinants of structural stability and substrate selectivity on the Ni-F₄₃₀ cofactor. Furthermore, structure-based pharmacophore modeling defined essential features (AAADDNNN and AAADDNN) that drive ligand binding. These findings provide a high-resolution molecular framework for the rational design of specific Mcr inhibitors, offering a robust starting point for developing broad-spectrum strategies to suppress enteric methane emissions. Full article

Helicobacter pylori urease (HpU) plays a central role in bacterial survival and virulence by hydrolyzing urea into ammonia and carbon dioxide, neutralizing gastric acidity, and facilitating host colonization. The increasing prevalence of antibiotic resistance underscores the need for alternative strategies targeting essential bacterial enzymes such as urease. In this study, a multistage computational pipeline integrating pharmacophore modeling, machine learning (ML), ensemble docking, and enhanced molecular dynamics simulations were applied to identify novel triazole-based HpU inhibitors. Starting from over seven million compounds in the ZINC15 database, pharmacophore- and ML-based filters progressively reduced the chemical space to 7062 candidates. Ensemble docking across 25 conformational frames of HpU, followed by quantum-polarized ligand docking (QPLD), identified seven promising ligands exhibiting strong binding energies and stable metal coordination. Molecular dynamics (MD) simulations under progressively relaxed restraints revealed three highly stable complexes (CA1, CA3, and CA6). Subsequent well-tempered metadynamics (WT-MetaD) simulations reconstructed free-energy landscapes showing deep, localized basins for CA3 and CA6, comparable to the potent reference inhibitor DJM, supporting their potential as strong urease binders. Finally, unsupervised chemical space mapping using the UMAP algorithm positioned these candidates within molecular regions associated with potent urease inhibitors, further validating their structural coherence and pharmacophoric relevance. An ADMET assessment confirmed that the selected candidates exhibit physicochemical and early safety properties compatible with subsequent in vitro evaluation. This multilevel screening strategy demonstrates the power of combining ML-driven classification, ensemble docking, and enhanced sampling simulations to discover non-hydroxamic urease inhibitors. Although the current findings are computational, they provide a rational foundation for future in vitro validation and for expanding the discovery of triazole-based scaffolds targeting ureolytic enzymes. 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

Background/Objectives: In this work, a series of six new indazole-benzimidazole hybrids (M1–M6) were designed, synthesized, and fully characterized. The design of these compounds was based on the combination of two pharmacophoric units, indazole and benzimidazole, both known for their broad spectrum of biological activities. Methods: The molecular hybridization strategy was planned to combine these scaffolds through an effective synthetic pathway, using 6-nitroindazole, two 2-mercaptobenzimidazoles, and 1,3- or 1,5-dihaloalkanes as key precursors, affording the desired hybrids in good yields and with enhanced biological activity. Quantum chemical calculations were performed to investigate the structural, electronic, and electrostatic properties of M1–M6 molecules using Density Functional Theory (DFT) at the B3LYP/6-311++G(d,p) level. The antimicrobial activity efficacy of these compounds was assessed in vitro against four Gram-positive bacteria (Staphylococcus aureus, Enterococcus faecalis, Bacillus cereus, and Lactobacillus plantarum), four Gram-negative bacteria (Salmonella enteritidis, Escherichia coli, Campylobacter coli, Campylobacter jejuni), and four fungal strains (Saccharomyces cerevisiae, Candida albicans, Candida tropicalis, and Candida glabrata) using ampicillin and tetracycline as reference standard drugs. Results: Among the series, compound M6 exhibited remarkable antimicrobial activity, with minimum inhibitory concentrations (MIC) of 1.95 µg/mL against S. cerevisiae and C. tropicalis, and 3.90 µg/mL against S. aureus, B. cereus, and S. enteritidis, while the standards Ampicillin (AmB) (MIC ≥ 15.62 µg/mL) and Tetracycline (TET) (MIC ≥ 7.81 µg/mL) exhibited higher MIC values. To gain molecular insights into the compounds, an in silico docking study was performed to determine the interactions of M1–M6 ligands against the antimicrobial target beta-ketoacyl-acyl carrier protein (ACP) synthase III complexed with malonyl-COA (PDB ID: 1HNJ). Molecular modeling data provided valuable information on the structure-activity relationship (SAR) and the binding modes influencing the candidate ligand-protein recognition. Amino acid residues, such as Arg249, located in the solvent-exposed region, were essential for hydrogen bonding with the nitro group of the 6-nitroindazole moiety. Furthermore, polar side chains such as Asn274, Asn247, and His244 participated in interactions mediated by hydrogen bonding with the 5-nitrobenzimidazole moiety of these compound series. Conclusions: The hybridization of indazole and benzimidazole scaffolds produced compounds with promising antimicrobial activity, particularly M6, which demonstrated superior potency compared to standard antibiotics. Computational and docking analyses provided insights into the structure–activity relationships, highlighting these hybrids as potential candidates for antimicrobial drug development. Full article

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease, affecting millions of people and challenging the public health framework globally. While the definitive cause of AD remains unclear, researchers are concentrating their efforts on several prominent theories. Currently, there are very few FDA-approved medications for AD, and these primarily alleviate symptoms rather than alter the disease’s progression. In response, research efforts focus on developing new medicines that address the complex nature of AD through multi-targeted approaches. Multitarget-directed ligands (MTDLs) are a promising treatment strategy for AD, despite the significant challenges they pose. This review examines recent advancements in designing prospective targeted MTDLs to combat AD, with a focus on categorizing the lead generation process and investigating the integration methods of key pharmacophores within the 2024–2025 timeframe. The review highlights numerous examples of novel MTDLs that address various AD hallmarks, demonstrating their broad therapeutic potential. These targets and activities include cholinesterase (AChE and/or BuChE) inhibition, monoamine oxidase (MAO-A and/or MAO-B) inhibition, antioxidant activity, amyloid-beta (Aβ) aggregation inhibition, tau protein aggregation inhibition, glycogen synthase kinase 3β (GSK-3β) inhibition, calcium channel blockade, anti-inflammatory activity, and other hallmarks. Full article

Background/Objectives: Janus kinase 2 (JAK2) is a pivotal signaling protein implicated in various hematological malignancies and inflammatory disorders, making it a compelling target for therapeutic intervention. Methods: In this study, we employed an integrative computational approach combining ligand-based screening, pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations, and MM/PBSA free energy calculations to identify JAK2 inhibitors from the ChEMBL database. A comprehensive virtual screening of over 1,900,000 compounds was conducted using Tanimoto similarity and a validated pharmacophore model, resulting in the identification of 39 structurally promising candidates. Docking analyses prioritized compounds with favorable interaction energies, while MD simulations over 100 ns assessed the dynamic behavior and binding stability of top hits. Results: Four compounds, CHEMBL4169802, CHEMBL4162254, CHEMBL4286867, and CHEMBL2208033, exhibited consistently superior performance, forming stable hydrogen bonds, favorable RMSD profiles (≤0.5 nm), and strong binding interactions, including salt bridges. Notably, the binding free energies revealed ΔG values as low as −29.91 kcal/mol, surpassing that of the reference inhibitor, momelotinib (−24.17 kcal/mol). Conclusions: Among these, CHEMBL4169802 emerged as the most promising candidate due to its synergistic electrostatic and hydrophobic interactions. Collectively, our results highlight these compounds as probable, JAK2-selective inhibitors with strong potential for further biological validation and optimization. Full article