Background: Middle East Respiratory Syndrome Coronavirus (MERS-COV) is the main cause of lung and kidney infections in developing countries such as Saudi Arabia and South Korea. This infectious single-stranded, positive (+) sense RNA virus enters the host by binding to dipeptidyl-peptide receptors. Since no vaccine is yet available for MERS-COV, rapid case identification, isolation, and infection prevention strategies must be used to combat the spreading of MERS-COV infection. Additionally, there is a desperate need for vaccines and antiviral strategies. Methods: The present study used immuno-informatics and computational approaches to identify conserved B- and T cell epitopes for the MERS-COV spike (S) protein that may perform a significant role in eliciting the resistance response to MERS-COV infection. Results: Many conserved cytotoxic T-lymphocyte epitopes and discontinuous and linear B-cell epitopes were predicted for the MERS-COV S protein, and their antigenicity and interactions with the human leukocyte antigen (HLA) B7 allele were estimated. Among B-cell epitopes, QLQMGFGITVQYGT displayed the highest antigenicity-score, and was immensely immunogenic. Among T-cell epitopes, MHC class-I peptide YKLQPLTFL and MHC class-II peptide YCILEPRSG were identified as highly antigenic. Furthermore, docking analyses revealed that the predicted peptides engaged in strong bonding with the HLA-B7 allele. Conclusion: The present study identified several MERS-COV S protein epitopes that are conserved among various isolates from different countries. The putative antigenic epitopes may prove effective as novel vaccines for eradication and combating of MERS-COV infection.

The dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a novel, promising and emerging biological target for therapeutic intervention in neurodegenerative diseases, especially in Alzheimer’s disease (AD). The molMall database, comprising rare, diverse and unique compounds, was explored for molecular docking-based virtual screening against the DYRK1A protein, in order to find out potential inhibitors. Ligands exhibiting hydrogen bond interactions with key amino acid residues such as Ile165, Lys188 (catalytic), Glu239 (gk+1), Leu241 (gk+3), Ser242, Asn244, and Asp307, of the target protein, were considered potential ligands. Hydrogen bond interactions with Leu241 (gk+3) were considered key determinants for the selection. High scoring structures were also docked by Glide XP docking in the active sites of twelve DYRK1A related protein kinases, viz. DYRK1B, DYRK2, CDK5/p25, CK1, CLK1, CLK3, GSK3β, MAPK2, MAPK10, PIM1, PKA, and PKCα, in order to find selective DYRK1A inhibitors. MM/GBSA binding free energies of selected ligand–protein complexes were also calculated in order to remove false positive hits. Physicochemical and pharmacokinetic properties of the selected six hit ligands were also computed and related with the proposed limits for orally active CNS drugs. The computational toxicity webserver ProTox-II was used to predict the toxicity profile of selected six hits (molmall IDs 9539, 11352, 15938, 19037, 21830 and 21878). The selected six docked ligand–protein systems were exposed to 100 ns molecular dynamics (MD) simulations to validate their mechanism of interactions and stability in the ATP pocket of human DYRK1A kinase. All six ligands were found to be stable in the ATP binding pocket of DYRK1A kinase.

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.

Mucus is the first biological component inhaled drugs encounter on their journey towards their pharmacological target in the upper airways. Yet, how mucus may influence drug disposition and efficacy in the lungs has been essentially overlooked. In this study, a simple in vitro system was developed to investigate the factors promoting drug interactions with airway mucus in physiologically relevant conditions. Thin layers of porcine tracheal mucus were prepared in Transwell^® inserts and initially, the diffusion of various fluorescent dyes across those layers was monitored over time. A deposition system featuring a MicroSprayer^® aerosolizer was optimized to reproducibly deliver liquid aerosols to multiple air-facing layers and then exploited to compare the impact of airway mucus on the transport of inhaled bronchodilators. Both the dyes and drugs tested were distinctly hindered by mucus with high logP compounds being the most affected. The diffusion rate of the bronchodilators across the layers was in the order: ipratropium ≈ glycopyronnium > formoterol > salbutamol > indacaterol, suggesting hydrophobicity plays an important role in their binding to mucus but is not the unique parameter involved. Testing of larger series of compounds would nevertheless be necessary to better understand the interactions of inhaled drugs with airway mucus.

The discovery of multi-targeted kinase inhibitors emerged as a potential strategy in the therapy of multi-genic diseases, such as cancer, that cannot be effectively treated by modulating a single biological function or pathway. The current work presents an extension of our effort to design and synthesize a series of new quinazolin-4-one derivatives based on their established anti-cancer activities as inhibitors of multiple protein kinases. The cytotoxicity of the new derivatives was evaluated against a normal human cell line (WI-38) and four cancer lines, including HepG2, MCF-7, MDA-231, and HeLa. The most active compound, 5d, showed broad-spectrum anti-cancer activities against all tested cell lines (IC₅₀ = 1.94–7.1 µM) in comparison to doxorubicin (IC₅₀ = 3.18–5.57 µM). Interestingly, compound 5d exhibited lower toxicity in the normal WI-38 cells (IC₅₀ = 40.85 µM) than doxorubicin (IC₅₀ = 6.72 µM), indicating a good safety profile. Additionally, the potential of compound 5d as a multi-targeted kinase inhibitor was examined against different protein kinases, including VEGFR2, EGFR, HER2, and CDK2. In comparison to the corresponding positive controls, compound 5d exhibited comparable activities in nanomolar ranges against HER2, EGFR, and VEGFR2. However, compound 5d was the least active against CDK2 (2.097 ± 0.126 µM) when compared to the positive control roscovitine (0.32 ± 0.019 µM). The apoptotic activity investigation in HepG2 cells demonstrated that compound 5d arrested the cell cycle at the S phase and induced early and late apoptosis. Furthermore, the results demonstrated that the apoptosis pathway was provoked due to an upregulation in the expression of the proapoptotic genes caspase-3, caspase-9, and Bax and the downregulation of the Bcl-2 anti-apoptotic gene. For the in silico docking studies, compound 5d showed relative binding interactions, including hydrogen, hydrophobic, and halogen bindings, with protein kinases that are similar to the reference inhibitors.