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.

Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies.

Objectives: Schistosomiasis, a neglected tropical disease, is a leading cause of mortality in affected geographic areas. Currently, because no vaccine for schistosomiasis is available, control measures rely on widespread administration of the drug praziquantel (PZQ). The mass administration of PZQ has prompted concerns regarding the emergence of drug resistance. Therefore, new therapeutic targets and potential compounds are necessary to combat schistosomiasis. Methods: Twenty-four potent derivatives of PZQ were optimized via density functional theory (DFT) at the B3LYP/6-31G∗ level. Quantitative structureactivity relationship (QSAR) models were generated and statistically validated, and a lead candidate was selected to develop therapeutic options with improved efficacy against schistosomiasis. The biological and binding energies of the designed compounds were evaluated. In addition, molecular dynamics; drug-likeness; absorption, distribution, metabolism, excretion, and toxicity (ADMET); and DFT studies were performed on the newly designed compounds. Results: Five QSAR models were generated, among which model 1 had favorable validation parameters (R2train: 0.957, R2adj: 0.941, LOF: 0.101, Q2cv: 0.906, and R2test: 0.783) and was chosen to identify a lead candidate. Other statistical parameters for the chosen model included variance inflation factor values ranging from 1.242 to 1.678, and a Y-scrambling coefficient (cRp2) of 0.747. Five new compounds were designed with improved predicted activity (ranging from 5.081 to 7.022) surpassing those of both the lead compound and PZQ (predicted pEC50 of 5.545). Molecular dynamics simulation revealed high binding affinity of the proposed compounds toward the target receptor. ADMET and drug-likeness assessments indicated adherence to Lipinski's rule of five criteria, thereby suggesting pharmacological and oral safety. In addition, DFT analysis indicated resistance to electronic alteration during chemical reactions. Conclusion: The proposed compounds exhibited potential drug characteristics, thus indicating their suitability for further investigation to enhance schistosomiasis treatment options.

Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models.

Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERß dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERß agonists among OH-PCB congeners.

Activity prediction, structure-based drug design, molecular docking, and pharmacokinetic studies of 1,4-dihydropyridines derivatives as α-amylase inhibitors.

Objectives: Diabetes places a substantial economic burden on countries worldwide. The costs associated with diabetes management, including healthcare services, medications, monitoring equipment, and productivity losses, are substantial. The International Diabetes Federation has estimated that global healthcare expenditures associated with diabetes and its complications exceed hundreds of billions of dollars annually. Therefore, a critical need exists to develop drugs that are highly effective, affordable, and easily accessible to society. Methods: This study explored the structural modification of 1,4-DHP derivatives to identify specific α-amylase inhibitors, with the aim of developing more effective and accessible drugs for diabetes. We evaluated the activity and binding ability of the designed compounds. In addition, we performed drug-likeness and pharmacokinetic studies on the modified compounds. Results: Equation (1) had the highest accuracy, on the basis of internal and external assessment parameters, including R2int = 0.852, R2adj = 0.803, Q2cv = 0.731, and R2ext = 0.884. Moreover, the five potent analogs identified through structure-based drug design demonstrated a more favorable interaction than observed for the template or acarbose. Additionally, comprehensive studies on the drug-like properties and pharmacokinetics of the designed compounds supported their oral safety and favorable pharmacokinetic profiles. Conclusions: The designed analogs show promise for developing new hypoglycemic agents. Their positive attributes and performance suggest that they may potentially serve as candidates for further research in improving treatments for high blood sugar-associated conditions.

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.

2,4-disubstituted 6-fluoroquinolines as potent antiplasmodial agents: QSAR, homology modeling, molecular docking and ADMET studies.

Objective: This work was designed to study 2,4-disubstituted 6-fluoroquinolines as antiplasmodial agents by using in silico techniques, to aid in the design of novel analogs with high potency against malaria and high inhibition of Plasmodium falciparum translation elongation factor 2 (PfeEF2), a novel drug target. Methods: Quantitative structure-activity relationships (QSAR) of 2,4-disubstituted 6-fluoroquinolines were studied with the genetic function approximation technique in Material Studio software. The 3D structure of PfeEF2 was modeled in the SWISS-MODEL workspace through homology modeling. A molecular docking study of the modeled PfeEF2 and 2,4-disubstituted 6-fluoroquinolines was conducted with Autodock Vina in Pyrx software. Furthermore, the in silico pharmacokinetic properties of selected compounds were investigated. Results: A robust, reliable and predictive QSAR model was developed that related the chemical structures of 2,4-disubstituted 6-fluoroquinolines to their antiplasmodium activities. The model had an internal squared correlation coefficient R2 of 0.921, adjusted squared correlation coefficient R2adj of 0.878, leave-one-out cross-validation coefficient Q2cv of 0.801 and predictive squared correlation coefficient R2pred of 0.901. The antiplasmodium activity of 6-fluoroquinolines was found to depend on the n5Ring, GGI9, TDB7u, TDB8u and RDF75i physicochemical properties: n5Ring, TDB8u and RDF75i were positively associated, whereas GGI9 and TDB7u were negatively associated, with the antiplasmodium activity of the compounds. Stable complexes formed between the compounds and modeled PfeEF2, with binding affinity ranging from -8.200 to -10.700 kcal/mol. Compounds 5, 11, 16, 22 and 24 had better binding affinities than quinoline-4-carboxamide (DDD107498), as well as good pharmacokinetic properties, and therefore may be better inhibitors of this novel target. Conclusion: QSAR and docking studies provided insight into designing novel 2,4-disubstituted 6-fluoroquinolines with high antiplasmodial activity and good structural properties for inhibiting a novel antimalarial drug target.

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.

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.

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.].

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.

Homology modeling, docking, and ADMET studies of benzoheterocyclic 4-aminoquinolines analogs as inhibitors of Plasmodiumfalciparum.

Objectives: The ongoing fight against endemic diseases is necessary due to the growing resistance of malarial parasites to widely accessible medications. Thus, there has been an ongoing search for antimalarial medications with improved efficacy. The goal of this study was to develop derivatives of benzoheterocyclic 4-aminoquinolines with enhanced activities and better binding affinities than the original compounds. Methods: Thirty-four derivatives of benzoheterocyclic 4-aminoquinolines were docked (using a model of dihydrofolate reductase-thymidylate synthase [DRTS] protein) with Molegro software to identify the compound with the minimum docking score as a design template. The generated quantitative structure-activity model was employed to estimate the activity of the designed derivatives. The derivatives were also docked to determine the most stable derivatives. Furthermore, the designed derivatives were tested for their drug-likeness and pharmacokinetic properties using SwissADME software and pkCSM web application, respectively. Results: Compound H-014, (N-(7-chloroquinolin-4-yl)-2-(4-methylpiperazin-1-yl)-1,3-benzoxazol-5-amine) with the lowest re-rank score of -115.423 was employed as the design template. Then 10 derivatives were further designed by substituting -OH, -OCH3, -CHO, -F, and -Cl groups at various positions of the template. We found that the designed derivatives had improved activities compared to the template. The docking scores of the designed derivatives were lower than those of the original derivatives. Derivative h-06 (7-methoxy-4-((2-(4-methylpiperazin-1-yl)benzo[d]oxazol-5-yl)amino)quinolin-6-ol) with four hydrogen bonds was identified as the most stable due to its lowest re-rank score (-163.607). While all of the designed derivatives satisfied both the Lipinski and Verber rules, some derivatives such as h-10 (cytochrome P450 1A2 [CYP1A2]); h-05, h-08, h-09, and h-10 [CYP2C19]; and h-03, h-07, h-08, and h-10 [renal organic cation transporter 2 substrate]) showed poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. Conclusion: Ten derivatives of benzoheterocyclic 4-aminoquinolines were designed with improved efficacies. Derivatives that follow Lipinski and Verber rules and are mostly non-toxic and non-sensitive to the skin can be utilized in the development of effective antimalarial medications.

Virtual screening, pharmacokinetic, and DFT studies of anticancer compounds as potential V600E-BRAF kinase inhibitors.

Objectives: V600E-BRAF kinase is an essential therapeutic target in melanoma and other types of tumors. Because of its resistance to known inhibitors and the adverse effects of some identified inhibitors, investigation of new potent inhibitors is necessary. Methods: In the present work, in silico strategies such as molecular docking simulation, pharmacokinetic evaluation, and density functional theory (DFT) computations were used to identify potential V600E-BRAF inhibitors from a set of 72 anticancer compounds in the PubChem database. Results: Five top-ranked molecules (12, 15, 30, 31, and 35) with excellent docking scores (MolDock score ≥90 kcal mol-1, Rerank score ≥60 kcal mol-1) were selected. Several potential binding interactions were discovered between the molecules and V600E-BRAF. The formation of H-bonds and hydrophobic interactions with essential residues of V600E-BRAF suggested the high stability of these complexes. The selected compounds had excellent pharmacological properties according to the drug likeness rules (bioavailability) and pharmacokinetic properties. Similarly, the energy for the frontier molecular orbitals, such as the HOMO, LUMO, energy gap, and other reactivity parameters, was computed with DFT. The frontier molecular orbital surfaces and electrostatic potentials were investigated to demonstrate the charge-density distributions potentially associated with anticancer activity. Conclusion: The identified compounds were found to be potent hit compounds for V600E-BRAF inhibition with superior pharmacokinetic properties; therefore, they may be promising cancer drug candidates.

New flavone-based arylamides as potential V600E-BRAF inhibitors: Molecular docking, DFT, and pharmacokinetic properties.

Objectives: The V600E-BRAF protein kinase is an attractive and essential therapeutic target in melanoma and other tumors. However, because of its resistance to the known inhibitors and side effects of some identified inhibitors, new potent inhibitors need to be identified. Methods: In the present work, in silico strategies such as the molecular docking simulation, DFT (Density-Functional-Theory) computations, and pharmacokinetic evaluation were used to determine potential V600E-BRAF inhibitors from a set of 31 synthesized novel flavone-based arylamides. Results: The docking result demonstrated that four compounds (10, 11, 28, and 31) had acceptable docking scores (MolDock score of -167.523 kcal mol-1, -158.168 kcal mol-1, -160.581 kcal mol-1,-162.302 kcal mol-1, and a Rerank score of -124.365, -129.365, -135.878 and -117.081, respectively) appeared as most active and potent V600E-BRAF inhibitors that topped vemurafenib (-158.139 and -118.607 kcal mol-1). The appearance of H-bonds and hydrophobic interactions with essential residues for V600E-BRAF proved the high stability of these complexes. The energy for the frontier molecular orbitals such as HOMO, LUMO, energy gap, and other reactivity parameters was computed using DFT. The frontier molecular-orbital surfaces and electrostatic potentials (EPs) were investigated to demonstrate the charge-density distributions that might be linked to anticancer activity. Similarly, the chosen compounds revealed superior pharmacological properties according to the drug-likeness rules (bioavailability) and pharmacokinetic properties. Conclusion: The chosen compounds were recognized as potent V600E-BRAF inhibitors with superior pharmacokinetic properties and could be promising cancer drug candidates.

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.

Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies.

PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.