Exploring the potential of 2-arylbenzimidazole scaffolds as novel α-amylase inhibitors: QSAR, molecular docking, simulation and pharmacokinetic studies.

Previous studies have shown that 2-arylbenzimidazole derivatives have a strong anti-diabetic effect. To further explore this potential, we develop new analogues of the compound using ligand-based drug design and tested their inhibitory and binding properties through QSAR analyses, molecular docking, dynamic simulations and pharmacokinetic studies. By using quantitative structure activity relationship and ligand-based modification, a highly precise predictive model and design of potent compounds was developed from the derivatives of 2-arylbenzimidazoles. Molecular docking and simulation studies were then conducted to identify the optimal binding poses and pharmacokinetic profiles of the newly generated therapeutic drugs. DFT was employed to optimize the chemical structures of 2-arylbenzimidazole derivatives using B3LYP/6-31G* as the basis set. The model with the highest R2trng set, R2adj, Q2cv, and R2test sets (0.926, 0.912, 0.903, and 0.709 respectively) was chosen to predict the inhibitory activities of the derivatives. Five analogues designed using ligand-based strategy had higher activity than the hit molecule. Additionally, the designed molecules had more favorable MolDock scores than the hit molecule and acarbose and simulation studies confirm on their stability and binding affinities towards the protein. The ADME and druglikeness properties of the analogues indicated that they are safe to consume orally and have a high potential for total clearance. The results of this study showed that the suggested analogues could act as α-amylase inhibitors, which could be used as a basis for the creation of new drugs to treat type 2 diabetes mellitus.

Vanadocene-functionalized mesoporous silica nanoparticles: platforms for the development of theranostic materials against breast cancer.

Nanoscale materials have demonstrated a very high potential in anticancer therapy by properly adjusting their functionalization and physicochemical properties. Herein, we report the synthesis of some novel vanadocene-loaded silica-based nanomaterials incorporating four different S-containing amino acids (penicillamine, methionine, captopril, and cysteine) and different fluorophores (rhodamine B, coumarin 343 or Alexa Fluor™ 647), which have been characterized by diverse solid-state spectroscopic techniques viz; FTIR, diffuse reflectance spectroscopies,13C and51V solid-state NMR spectroscopy, thermogravimetry and TEM. The analysis of the biological activity of the novel vanadocene-based nanostructured silicas showed that the materials containing cysteine and captopril aminoacids demonstrated high cytotoxicity and selectivity against triple negative breast cancer cells, making them very promising antineoplastic drug candidates. According to the biological results it seems that vanadium activity is connected to its incorporation through the amino acid, resulting in synergy that increases the cytotoxic activity against cancer cells of the studied materials presumably by increasing cell internalization. The results presented herein hold significant potential for future developments in mesoporous silica-supported metallodrugs, which exhibit strong cytotoxicity while maintaining low metal loading. They also show potential for theranostic applications highlighted by the analysis of the optical properties of the studied systems after incorporating rhodamine B, coumarin 343 (possible)in vitroanticancer analysis, or Alexa Fluor™ 647 (in vivostudies of cancer models).

Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis.

The quest for a sound treatment on the vulnerable population suffering and dying as a result of the blood flukes, S. mansoni is on the increase because both Praziquantel and Oxamniquine widely used for the treatment of Schistosomiasis for over 51 years suffer resistance and recurrence. Here-in, chemo-informatics techniques such as QSAR modeling, pharmacokinetic, docking alongside MD simulation were harnessed in designing novel 7-keto- sempevirolsempevirol derivatives that are more competent against S. mansoni. Upon QSAR screening, compound 15, which appears to be in the model's acceptability space, emerges the best with a high predicted activity. 5 new analogues with improved activity against Schistosomiasis better than the standard drug PZQ were designed from compound 15 (template 15*) on an account of the descriptors significance from the model with robust and validated parameters. Also their pharmacokinetic profiles indicates that the designed compounds have the characteristics of a good drug. Furthermore, docking evaluation fulfilled ranges from -113.121 to -100.79 kcal/mol (moldock score), with compound U1 being the best (least moldock score of -113.121 compared to PZQ and 15* (template) having a moldock score value of (-87.21 and -83.37 kcal/mol). 100-ns MD Simulation on the U1-docked complex was run using Desmond 2019-4 package. The nature and steadiness of U1 compound within the enzyme active site was further confirmed by RMSD, RMSF, RoG and H-bond assessment. Hence, we recommend compound U1 targeting the SmCB1 enzyme (6YI7) for Schistosomiasis treatment and for further medicinal evaluation and utilization.

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents.

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

Molecular modelling studies of substituted indole derivatives as novel influenza a virus inhibitors.

The emergence of drug-resistant strains motivate researchers to find new innovative anti-IAV candidates with a different mode of action. In this work, molecular modelling strategies, such as 2D-QSAR, 3D-QSAR, molecular docking, molecular dynamics, FMOs, and ADMET were applied to some substituted indoles as IAV inhibitors. The best-developed 2D-QSAR models, MLR (Q2 = 0.7634, R2train = 0.8666) and ANN[4-3-1] (Q2 = 0.8699, R2train = 0.8705) revealed good statistical validation for the inhibitory response predictions. The 3D-QSAR models, CoMFA (Q2 = 0.504, R2train = 0.805) and CoMSIA/SEDHA (Q2 = 0.619, R2train = 0.813) are selected as the best 3D models following the global thresholds. In addition, the contour maps generated from the CoMFA and CoMSIA models illustrate the relationship between the molecular fields and the inhibitory effects of the studied molecules. The results of the studies led to the design of five new molecules (24a-e) with enhanced anti-IAV activities and binding potentials using the most active molecule (24) as the template scaffold. The conformational stability of the best-designed molecules with the NA protein showed hydrophobic and H-bonds with the key residues from the molecular dynamics simulations of 100 ns. Furthermore, the global reactivity indices from the DFT calculations portrayed the relevance of 24c in view of its smaller band gap as also justified by our QSAR and molecular simulation studies.Communicated by Ramaswamy H. Sarma.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis.

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 (R2train = 0.8646, R2test = 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.

Retraction notice to "In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions" [Heliyon 8, 2022 Article e10101].

[This retracts the article DOI: 10.1016/j.heliyon.2022.e10101.].

Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease.

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.

Ligand-based drug design of quinazolin-4(3H)-ones as breast cancer inhibitors using QSAR modeling, molecular docking, and pharmacological profiling.

BACKGROUND: Breast cancer is the most common tumor among females globally. Its prevalence is growing around the world, and it is alleged to be the leading cause of cancer death. Approved anti-breast cancer drugs display several side effects and resistance during the early treatment stage. Hence, there is a need for the development of more effective and safer drugs. This research was aimed at designing more potent quinazolin-4(3H)-one molecules as breast cancer inhibitors using a ligand-based design approach, studying their modes of interaction with the target enzyme using molecular docking simulation, and predicting their pharmacological properties. METHODS: The QSAR model was developed using a series of quinazoline-4(3H)-one derivatives by utilizing Material Studio v8.0 software and validated both internally and externally. Applicability domain virtual screening was utilized in selecting the template molecule, which was structurally modified to design more potent molecules. The inhibitive capacities of the design molecules were predicted using the developed model. Furthermore, molecular docking was performed with the EGFR target active site residues, which were obtained from the protein data bank online server (PDB ID: 2ITO) using Molegro Virtual Docker (MVD) software. SwissADME and pkCSM online sites were utilized in predicting the pharmacological properties of the designed molecules. RESULTS: Four QSAR models were generated, and the first model was selected due to its excellent internal and external statistical parameters as follows: R2 = 0.919, R2adj = 0.898, Q2cv = 0.819, and R2pred = 0.7907. The robustness of the model was also confirmed by the result of the Y-scrambling test performed with cR2p = 0.7049. The selected model was employed to design seven molecules, with compound 4 (pIC50 = 5.18) adopted as the template. All the designed compounds exhibit better activities ranging from pIC50 = 5.43 to 5.91 compared to the template and Doruxybucin (pIC50 = 5.35). The results of molecular docking revealed better binding with the EGFR target compared with the template and Doruxybucin. The designed compounds exhibit encouraging therapeutic applicability, as evidenced by the findings of pharmacological property prediction. CONCLUSIONS: The designed derivatives could be utilized as novel anti-breast cancer agents.

Structure-based drug design, molecular dynamics simulation, ADMET, and quantum chemical studies of some thiazolinones targeting influenza neuraminidase.

The genetic mutability of the influenza virus leads to the existence of drug-resistant strains which is dangerous, particularly with the lingering coronavirus disease (COVID-19). This necessitated the need for the search and discovery of more potential anti-influenza agents to avert future outbreaks. In furtherance of our previous in-silico studies on 5-benzyl-4-thiazolinones as anti-influenza neuraminidase (NA) inhibitors, molecule 11 was selected as the template scaffold for the structure-based drug design due to its good binding, pharmacokinetic profiling, and better NA inhibitory activity. As such, eighteen (18) new molecules (11a-r) were designed with better MolDock scores as compared with the template scaffold and the zanamivir reference drug. However, the dynamic stability of molecule 11a in the binding cavity of the NA target (3TI5) showed water-mediated hydrogen and hydrophobic bondings with the active residues such as Arg118, Ile149, Arg152, Ile222, Trp403, and Ile427 after the MD simulation for 100 ns. The drug-likeness and ADMET assessment of all designed molecules predicted non-violation of the stipulated thresholds of Lipinski's rule and good pharmacokinetic properties respectively. In addition, the quantum chemical calculations also suggested the significant chemical reactivity of molecules with their smaller band energy gap, high electrophilicity, high softness, and low hardness. The results obtained in this study proposed a reliable in-silico viewpoint for anti-influenza drug discovery and development.Communicated by Ramaswamy H. Sarma.

Computational design, molecular properties, ADME, and toxicological analysis of substituted 2,6-diarylidene cyclohexanone analogs as potent pyridoxal kinase inhibitors.

Leishmaniasis is one of the tropical diseases which affects over 12 million people mainly in the tropical regions of the world and is caused by the leishmanial parasites transmitted by the female sand fly. The lack of vaccines to prevent leishmaniasis, as well as limitations of existing therapies necessitated this study which was focused on a combined virtual docking screening and 3-D QSAR modeling approach to design some diarylidene cyclohexanone analogs, while also performing pharmacokinetic analysis and Molecular Dynamic (MD) simulation to ascertain their drug-ability. As a result, the built 3-D QSAR model was found to satisfy the requirement of a good model with R2 = 0.9777, SDEC = 0.0593, F-test = 105.028, and Q2 LOO = 0.6592. The template (compound 9, MolDock score = - 161.064) and all seven newly designed analogs were found to possess higher docking scores than the reference drug (Pentamidine, Moldock score = - 137.827). The results of the pharmacokinetic analysis suggest 9 and the new molecules (9a, b, c, e, and f) as orally bioavailable with good ADME and safe toxicological profiles. These molecules also showed good binding interactions with the receptor (pyridoxal kinase). Additionally, the MD simulation result confirmed the stability of the tested protein-ligand complexes, with an estimated ∆G binding (MM/GBSA) of - 65.2177 kcal/mol and - 58.433 kcal/mol for 9_6K91 and 9a_6K91 respectively. Hence, the new compounds, especially 9a could be considered potential anti-leishmanial inhibitors.

Pharmacokinetic profiling of quinazoline-4(3H)-one analogs as EGFR inhibitors: 3D-QSAR modeling, molecular docking studies and the design of therapeutic agents.

Objectives: Breast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents. Methods: Robust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software. Results: Internal validations of the best CoMFA and CoMSIA models (R2 = 0.855 and 0.895; Q2 = 0.570 and 0.599) passed the threshold values for the establishment of a consistent QSAR model. The constructed models were further validated externally using six compounds as a test set, thus revealing a satisfactory predicted correlation coefficient (R2 pred = 0.657 and 0.681). The CoMSIA_SHE models with the best statistical parameters were further subjected to applicability domain checks and only three influentials were detected. These were then utilized to design five novel compounds with activities ranging from 5.62 to 6.03. Molecular docking studies confirmed that compounds 20 to 26, with docking scores ranging from -163.729 to -169.796, represented lead compounds with higher docking scores compared to Gefitinib (-127.495). Furthermore, the designed compounds exhibited better docking scores ranging from -171.379 to -179.138. Conclusions: Pharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally.

Molecular docking simulation, drug-likeness assessment, and pharmacokinetic study of some cephalosporin analogues against a penicillin-binding protein of Salmonella typhimurium.

In pursuit of novel antibiotics that could curb the growing trend of multidrug resistance by Salmonella typhimurium, a data set of some cephalosporin analogues were subjected to Molecular Docking based virtual screening against a penicillin-binding protein (PBP 1b) of the bacterium to ascertain the binding affinity values of the bioactive ligands against the active sites of the PBP 1b protein target using the AutoDock Vina Software. Three compounds with binding affinity values ranging from -7.8 kcal/mol to -8.2 kcal/mol were selected as the most promising leads. The selected compounds also displayed better potencies against the bacterium when compared with Cefuroxime (binding affinity = -6.4 kcal/mol), a standard ß-lactam antibiotic used herein for quality control and assurance. Furthermore, evaluation of the drug-likeness and ADMET properties of the three most promising leads revealed that they possess good oral bioavailability and excellent pharmacokinetic profiles. It is hoped that the findings of this study will provide an excellent template for developing more potent ß-lactam antibiotics against Salmonella typhimurium.

Molecular docking-based virtual screening, drug-likeness, and pharmacokinetic profiling of some anti-Salmonella typhimurium cephalosporin derivatives.

Objective: The rising cases of resistance to existing antibiotic therapies in Salmonella typhimurium has made it necessary to search for novel drug candidates. The present study employed the molecular docking technique to screen a set of antibacterial cephalosporin analogues against penicillin-binding protein 1a (PBP1a) of the bacterium. This is the first study to screen cephalosporin analogues against PBP1a, a protein central to peptidoglycan synthesis in S. typhimurium. Methods: Some cephalosporin analogues were retrieved from a drug repository. The structures of the molecules were optimized using the semi-empirical method of Spartan 14 software and were subsequently docked against the active sites of PBP1a using AutoDock vina software. The most potent ligands were chosen as the most promising leads and subsequently subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling using the SwissADME online server and DataWarrior chemoinformatics program. The CABSflex 2.0 server was used to carry out molecular dynamics (MD) simulation on the most stable ligand-protein complex. Results: Compounds 3, 23, and 28 with binding affinity (ΔG) values of -9.2, -8.7, and -8.9 kcal/mol, respectively, were selected as the most promising leads. The ligands bound to the active sites of PBP1a via hydrophobic bonds, hydrogen bonds, and electrostatic interactions. Furthermore, ADMET analyses of the ligands revealed that they exhibited sound pharmacokinetic and toxicity profiles. In addition, an MD study revealed that the most active ligand bound favorably and dynamically to the target protein. Conclusion: The findings of this research could provide an excellent platform for the discovery and rational design of novel antibiotics against S. typhimurium. Additional in vitro and in vivo studies should be carried out on the drug candidates to validate the findings of this study.

Molecular docking-based virtual screening, molecular dynamic simulation, and 3-D QSAR modeling of some pyrazolopyrimidine analogs as potent anti-filarial agents.

Lymphatic filariasis and onchocerciasis are common filarial diseases caused by filarial worms, which co-habit symbiotically with the Wolbachia organism. One good treatment method seeks Wolbachia as a drug target. Here, a computer-aided molecular docking screening and 3-D QSAR modeling were conducted on a series of Fifty-two (52) pyrazolopyrimidine derivatives against four Wolbachia receptors, including a pharmacokinetics study and Molecular Dynamic (MD) investigation, to find a more potent anti-filarial drug. The DFT approach (B3LYP with 6-31G** option) was used for the structural optimization. Five ligand-protein interaction pairs with the highest binding affinities were identified in the order; 23_7ESX (-10.2 kcal/mol) > 14_6EEZ (- 9.0) > 29_3F4R (- 8.0) > 26_6W9O (- 7.7) ≈ doxycycline_7ESX (- 7.7), with good pharmacological interaction profiles. The built 3-D QSAR model satisfied the requirement of a good model with R2 = 0.9425, Q2 LOO = 0.5019, SDEC = 0.1446, and F test = 98.282. The selected molecules (14, 23, 26, and 29) perfectly obeyed Lipinski's RO5 for oral bio-availability, and showed excellent ADMET properties, except 14 with positive AMES toxicity. The result of the MD simulation showed the great stability associated with the binding of 23 onto 7ESX's binding pocket with an estimated binding free energy (MM/GBSA) of - 60.6552 kcal/mol. Therefore, 23 could be recommended as a potential anti-filarial drug molecule, and/or template for the design of more prominent inhibitors. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-022-00136-y.

Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Background: Influenza virus disease remains one of the most contagious diseases that aided the deaths of many patients, especially in this COVID-19 pandemic era. Recent discoveries have shown that the high prevalence of influenza and SARS-CoV-2 coinfection can rapidly increase the death rate of patients. Hence, it became necessary to search for more potent inhibitors for influenza disease therapy. The present study utilized some computational modeling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions of some 1,3-thiazine derivatives as inhibitors of influenza neuraminidase (NA). Results: The 2D-QSAR modeling results showed GFA-MLR ( R train 2 = 0.9192, Q 2 = 0.8767, R 2 adj = 0.8991, RMSE = 0.0959, R test 2 = 0.8943, R pred 2 = 0.7745) and GFA-ANN ( R train 2 = 0.9227, Q 2 = 0.9212, RMSE = 0.0940, R test 2 = 0.8831, R pred 2 = 0.7763) models with the computed descriptors as ATS7s, SpMax5_Bhv, nHBint6, and TDB9m for predicting the NA inhibitory activities of compounds which have passed the global criteria of accepting QSAR model. The 3D-QSAR modeling was carried out based on the comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA). The CoMFA_ES ( R train 2 = 0.9620, Q 2 = 0.643) and CoMSIA_SED ( R train 2 = 0.8770, Q 2 = 0.702) models were found to also have good and reliable predicting ability. The compounds were also virtually screened based on their binding scores via molecular docking simulations with the active site of the NA (H1N1) target receptor which also confirms their resilient potency. Four potential lead compounds (4, 7, 14, and 15) with the relatively high inhibitory rate (> 50%) and docking (> - 6.3 kcal/mol) scores were identified as the possible lead candidates for in silico exploration of improved anti-influenza agents. Conclusion: The drug-likeness and ADMET predictions of the lead compounds revealed non-violation of Lipinski's rule and good pharmacokinetic profiles as important guidelines for rational drug design. Hence, the outcome of this research set a course for the in silico design and exploration of novel NA inhibitors with improved potency.

In-silico modelling studies of 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase inhibitors via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions.

Influenza virus disease is one of the most infectious diseases responsible for many human deaths, and the high mutability of the virus causes drug resistance effects in recent times. As such, it became necessary to explore more inhibitors that could avert future influenza pandemics. The present research utilized some in-silico modelling concepts such as 2D-QSAR, 3D-QSAR, molecular docking simulation, and ADMET predictions on some 5-benzyl-4-thiazolinone derivatives as influenza neuraminidase (NA) inhibitors. The 2D-QSAR modelling results revealed GFA-MLR ( R train 2 =0.8414, Q2 = 0.7680) and GFA-ANN ( R train â€‹ 2 =0.8754, Q2 = 0.8753) models with the most relevant descriptors (MATS3i, SpMax5_Bhe, minsOH and VE3_D) for predicting the inhibitory activities of the molecules which has passed the global criteria of accepting QSAR models. The results of the 3D-QSAR modelling results showed that CoMFA_ES ( R train â€‹ 2 =0.9030, Q2 = 0.5390) and CoMSIA_EA ( R train 2 =0.880, Q2 = 0.547) models are having good predicting ability among other developed models. The molecules were virtually screened via molecular docking simulation with the active site of NA protein receptor (pH1N1) which confirms their resilient potency when compared with zanamivir standard drug. Molecule 11 as the most potent molecule formed more H-bond interactions with the key residues such as TRP178, ARG152, ARG292, ARG371, and TYR406 that triggered the catalytic reactions for NA inhibition. Furthermore, six (6) molecules (9, 10, 11, 17, 22, and 31) with relatively high inhibitory activities and docking scores were identified as the possible leads for in-silico exploration of novel NA inhibitors. The drug-likeness and ADMET predictions of the lead molecules revealed non-violation of Lipinski's rule and good pharmacokinetic profiles respectively, which are important guidelines for rational drug design. Hence, the outcome of this study overlaid a solid foundation for the in-silico design and exploration of novel NA inhibitors with improved potency.

QSAR, homology modeling, and docking simulation on SARS-CoV-2 and pseudomonas aeruginosa inhibitors, ADMET, and molecular dynamic simulations to find a possible oral lead candidate.

Background: 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. Results: 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: Q2 LOO = 0.8027; R 2 = 0.8735; R 2 ext = 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. Conclusion: 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. Supplementary Information: The online version contains supplementary material available at 10.1186/s43141-022-00362-z.

Theoretical modeling and design of some pyrazolopyrimidine derivatives as Wolbachia inhibitors, targeting lymphatic filariasis and onchocerciasis.

Lymphatic filariasis and onchocerciasis are two common filarial diseases caused by a group of parasitic nematodes called filarial worms, which play host to the bacteria organism Wolbachia. One good treatment approach seeks Wolbachia as drug target. Here, a QSAR study was conducted to investigate the anti-wolbachia activities (pEC50) of 52 pyrazolopyrimidine analogues, while using the built model to predict the pEC50 values of the newly designed analogues. Density Functional Theory was used for the structural optimization, while the model building was based on Genetic Function Algorithm approach. The built QSAR model was validated thus: R2 = 0.8104, R2 adj = 0.7629, Q2 cv = 0.6981, R2 test = 0.7501 and cRp2 = 0.7476. The predicted pEC50 of all newly designed compounds were higher than that of the template (43). The new compounds were; observed to pass the drug-likeness criteria, uniformly distributed to the brain, and found to be non-mutagenic. Also, the new compounds and the reference drug (doxycycline), were docked onto Ovarian Tumor (OTU) deubiquitinase receptor (PDB ID: 6W9O) using iGEMDOCK tool. This protein is known to help Wolbachia subvert host ubiquitin signaling. The resulting binding scores of the newly designed compounds except A5 were higher than that of doxycycline, while the protein-ligand interactions were majorly characterized by Hydrogen-bonding and hydrophobic interaction types. Therefore, the newly designed molecules could be developed as potential drug candidates for the treatment of lymphatic filariasis and onchocerciasis.