Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R²_train = 0.8646, R²_test = 0.8864, Q² = 0.5773) and the predicted inhibitory activity (pIC₅₀) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.Communicated by Ramaswamy H. Sarma.

In seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives. The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q2LOO = 0.8027; R2 = 0.8735; R2ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation—lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run. Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

A calculation analysis on the molecular structure and energy of 4-methyl-3-[2-(4-nitrophenyl)-1,3-dioxo-2,3-dihydro-1H-isoindole-5-amido]benzoic acid (COD30) is carried out with the 6-311G (d,p) basis set by the DFT/RB3LYP method as an anti-graves' disease treatment. The calculated FT-IR spectrum is strongly correlated with the vibrational spectra reported in the literature. To evaluate the entire electron density and organic reactive sites of COD30, molecular electrostatic potential (MEP) and frontier molecular orbitals (FMO) were analyzed. The density of states analysis is used to determine the orbital molecular contributions (DOS and PDOS). In comparison to methimazole (MMI) and propylthiouracil, COD30 showed more encouraging docking results, and it also offered golden binding contacts in addition to an improvement in docking energy (PTU). The outcomes of bioactivity prediction and MD simulation indicate that COD30 could be further developed into an inhibitor of Graves' disease.

The development of novel medicines to treat autoimmune diseases and SARS-CoV-2 main protease (Mpro), a virus that can cause both acute and chronic illnesses, is an ongoing necessity for the global community. The primary objective of this research is to use CoMFA methods to evaluate the quantitative structure-activity relationship (QSAR) of a select group of chemicals concerning autoimmune illnesses. By performing a molecular docking analysis, we may verify previously observed tendencies and gain insight into how receptors and ligands interact. The results of the 3D QSAR models are quite satisfactory and give significant statistical results: Q_loo^∧2 = 0.5548, Q_lto^∧2 = 0.5278, R^∧2 = 0.9990, F-test = 3,101.141, SDEC = 0.017 for the CoMFA FFDSEL, and Q_loo^∧2 = 0.7033, Q_lto^∧2 = 0.6827, Q_lmo^∧2 = 0.6305, R^∧2 = 0.9984, F-test = 1994.0374, SDEC = 0.0216 for CoMFA UVEPLS. The success of these two models in exceeding the external validation criteria used and adhering to the Tropsha and Glorbaikh criteria’s upper and lower bounds can be noted. We report the docking simulation of the compounds as an inhibitor of the SARS-CoV-2 Mpro and an autoimmune disorder in this context. For a few chosen autoimmune disorder receptors (protein tyrosine phosphatase, nonreceptor type 22 (lymphoid) isoform 1 (PTPN22), type 1 diabetes, rheumatoid arthritis, and SARS-CoV-2 Mpro, the optimal binding characteristics of the compounds were described. According to their potential for effectiveness, the studied compounds were ranked, and those that demonstrated higher molecular docking scores than the reference drugs were suggested as potential new drug candidates for the treatment of autoimmune disease and SARS-CoV-2 Mpro. Additionally, the results of analyses of drug similarity, ADME (Absorption, Distribution, Metabolism, and Excretion), and toxicity were used to screen the best-docked compounds in which compound 4 scaled through. Finally, molecular dynamics (MD) simulation was used to verify compound 4’s stability in the complex with the chosen autoimmune diseases and SARS-CoV-2 Mpro protein. This compound showed a steady trajectory and molecular characteristics with a predictable pattern of interactions. These findings suggest that compound 4 may hold potential as a therapy for autoimmune diseases and SARS-CoV-2 Mpro.

Lysosomal membrane proteins (LAMPs) are a primary target for treating tumors because of their essential role in the cancer life cycle. In this study, some computational approaches, including drug-like screening, molecular docking, and molecular dynamics (MD) simulation studies coupled with the binding free energy, have been conducted to explore the putative binding modes of pyrazole derivatives as inhibitors of lysosomal storage disorders. Certain pyrazole derivatives outperformed typical medications in molecular docking experiments against the LAMPs receptor; among other substances, molecules CID 44555488 and 45,487,645 were deemed ideal. Additionally, these ligands (CID 44555488 and 45,487,645) were projected to be orally accessible in humans after successfully passing five separate drug-likeness criteria. In the end, it was anticipated that these ligands, CID 44555488 and 45,487,645, would have minimal human toxicity and good ADMET properties, particularly in terms of GI absorption and the lack of P-gp interaction. Compounds CID 44555488 and 45,487,645 with high predicted binding affinities were subjected to further molecular dynamics simulations based on the molecular docking data, and their potential binding mechanisms were investigated. The study's description of the structure-based drug design approach will be very helpful in the creation of novel inhibitors with excellent selectivity and potency.