Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling,Auto-QSAR,and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.     

Facile Route to Novel Pyrazolo[3,4‐d]pyrimidine,Imidazo[1,2‐b] pyrazole,Pyrazolo[3,4‐d][1,2,3]triazine,Pyrazolo[1,5‐c][1,3,5]triazine and Pyrazolo[1,5‐c][1,3,5]thiadiazine Derivatives

A regioselective synthesis of novel pyrazolo[3,4‐d]pyrimidines, imidazo[1,2‐b]pyrazoles, pyrazolo[3,4‐d][1,2,3]triazine, pyrazolo[1,5‐c][1,3,5]triazine and pyrazolo[1,5‐c][1,3,5]thiadiazine incorporating a thiazole moiety was described via the reactions of the versatile, readily accessible 5‐amino‐3‐(phenylamino)‐N‐(4‐phenylthiazol‐2‐yl)‐1H‐pyrazole‐4‐carboxamide ( 1 ) with each of DMF‐DMA, phenylisothiocyanate, chloroacetyl chloride, phenacyl bromide, benzoylisothiocyanate and formalin, respectively. All structures of the newly synthesized compounds were elucidated by elemental analysis and spectral data.     

3E-Net: Entropy-Based Elastic Ensemble of Deep Convolutional Neural Networks for Grading of Invasive Breast Carcinoma Histopathological Microscopic Images

Automated grading systems using deep convolution neural networks (DCNNs) have proven their capability and potential to distinguish between different breast cancer grades using digitized histopathological images. In digital breast pathology, it is vital to measure how confident a DCNN is in grading using a machine-confidence metric, especially with the presence of major computer vision challenging problems such as the high visual variability of the images. Such a quantitative metric can be employed not only to improve the robustness of automated systems, but also to assist medical professionals in identifying complex cases. In this paper, we propose Entropy-based Elastic Ensemble of DCNN models (3E-Net) for grading invasive breast carcinoma microscopy images which provides an initial stage of explainability (using an uncertainty-aware mechanism adopting entropy). Our proposed model has been designed in a way to (1) exclude images that are less sensitive and highly uncertain to our ensemble model and (2) dynamically grade the non-excluded images using the certain models in the ensemble architecture. We evaluated two variations of 3E-Net on an invasive breast carcinoma dataset and we achieved grading accuracy of 96.15% and 99.50%.     

Heterocyclic synthesis with ethyl α‐(3‐carbamoyl‐4,5,6,7‐tetrahydrobenzo[b]thiophen‐2‐ylhydrazono)acetates: An expeditious synthetic approach to polyfunctionally substituted pyran,pyridine, and pyridazine derivatives

Ethyl α‐(3‐carbamoyl‐4,5,6,7‐tetrahydrobenzo[b]thiophen‐2‐ylhydrazono)acetates 1 were prepared and established as previously described by our research group. Their reactivity toward a variety of active methylene reagents was studied to give pyran, pyridine, and pyridazine derivatives. © 2004 Wiley Periodicals, Inc. Heteroatom Chem 15:300–306, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/hc.20019     

Potentiometric Studies on SomeCephalosporin Complexes

Summary.  The interaction of Ca(II), Cu(II), Zn(II), Pb(II), and La(III) ions with the antibiotics cephalexin, cefadroxil, cephaloridine, and cefoperazone as secondary ligands was investigated potentiometrically. The formation constants were determined for a ligand-to-metal ratio of 1:1 at 25°C and KNO₃. The protonation constants of the complexes were evaluated for the system . The order of stability of the binary and ternary complexes were examined. It was found that glycine adds preferably [M(II)-cephalosporin] rather than to the aqueous complexes of M(II). In all cases 1:1:1 complexes were formed. Received February 4, 2000. Accepted (revised) May 10, 2000     

Increasing the Efficacy of Seproxetine as an Antidepressant Using Charge–Transfer Complexes

The charge transfer interactions between the seproxetine (SRX) donor and π-electron acceptors [picric acid (PA), dinitrobenzene (DNB), p-nitrobenzoic acid (p-NBA), 2,6-dichloroquinone-4-chloroimide (DCQ), 2,6-dibromoquinone-4-chloroimide (DBQ), and 7,7′,8,8′-tetracyanoquinodi methane (TCNQ)] were studied in a liquid medium, and the solid form was isolated and characterized. The spectrophotometric analysis confirmed that the charge–transfer interactions between the electrons of the donor and acceptors were 1:1 (SRX: π-acceptor). To study the comparative interactions between SRX and the other π-electron acceptors, molecular docking calculations were performed between SRX and the charge transfer (CT) complexes against three receptors (serotonin, dopamine, and TrkB kinase receptor). According to molecular docking, the CT complex [(SRX)(TCNQ)] binds with all three receptors more efficiently than SRX alone, and [(SRX)(TCNQ)]-dopamine (CTcD) has the highest binding energy value. The results of AutoDock Vina revealed that the molecular dynamics simulation of the 100 ns run revealed that both the SRX-dopamine and CTcD complexes had a stable conformation; however, the CTcD complex was more stable. The optimized structure of the CT complexes was obtained using density functional theory (B-3LYP/6-311G++) and was compared.     

Synthesis of Cyano-Benzylidene Xanthene Synthons Using a Diprotic Brønsted Acid Catalyst,and Their Application as Efficient Inhibitors of Aluminum Corrosion in Alkaline Solutions

Novel cyano-benzylidene xanthene derivatives were synthesized using one-pot and condensation reactions. A diprotic Brønsted acid (i.e., oxalic acid) was used as an effective catalyst for the promotion of the synthesis process of the new starting xanthene–aldehyde compound. Different xanthene concentrations (ca. 0.1–2.0 mM) were applied as corrosion inhibitors to control the alkaline uniform corrosion of aluminum. Measurements were conducted in 1.0 M NaOH solution using Tafel extrapolation and linear polarization resistance (LPR) methods. The investigated xanthenes acted as mixed-type inhibitors that primarily affect the anodic process. Their inhibition efficiency values were enhanced with inhibitor concentration, and varied according to their chemical structures. At a concentration of 2.0 mM, the best-performing studied xanthene derivative recorded maximum inhibition efficiency values of 98.9% (calculated via the Tafel extrapolation method) and 98.4% (estimated via the LPR method). Scanning electron microscopy (SEM) was used to examine the morphology of the corroded and inhibited aluminum surfaces, revealing strong inhibitory action of each studied compound. High-resolution X-ray photoelectron spectroscopy (XPS) profiles validated the inhibitor compounds’ adsorption on the Al surface. Density functional theory (DFT) and Monte Carlo simulations were applied to investigate the distinction of the anticorrosive behavior among the studied xanthenes toward the Al (111) surface. The non-planarity of xanthenes and the presence of the nitrile group were the key players in the adsorption process. A match between the experimental and theoretical findings was evidenced.     

Multispectral and Molecular Docking Studies Reveal Potential Effectiveness of Antidepressant Fluoxetine by Forming π-Acceptor Complexes

Poor mood, lack of pleasure, reduced focus, remorse, unpleasant thoughts, and sleep difficulties are all symptoms of depression. The only approved treatment for children and adolescents with major depressive disorder (MDD) is fluoxetine hydrochloride (FXN), a serotonin selective reuptake inhibitor antidepressant. MDD is the most common cause of disability worldwide. In the present research, picric acid (PA); dinitrobenzene; p-nitro benzoic acid; 2,6-dichloroquinone-4-chloroimide; 2,6-dibromoquinone-4-chloroimide; and 7,7′,8,8′-tetracyanoquinodimethane were used to make 1:1 FXN charge-transfer compounds in solid and liquid forms. The isolated complexes were then characterized by elemental analysis, conductivity, infrared, Raman, and ¹H-NMR spectra, thermogravimetric analysis, scanning electron microscopy, and X-ray powder diffraction. Additionally, a molecular docking investigation was conducted on the donor moiety using FXN alone and the resulting charge transfer complex [(FXN)(PA)] as an acceptor to examine the interactions against two protein receptors (serotonin or dopamine). Interestingly, the [(FXN)(PA)] complex binds to both serotonin and dopamine more effectively than the FXN drug alone. Furthermore, [(FXN)(PA)]–serotonin had a greater binding energy than [FXN]–serotonin. Theoretical data were also generated by density functional theory simulations, which aided the molecular geometry investigation and could be beneficial to researchers in the future.     

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.     

Interactive Effects of Molybdenum,Zinc and Iron on the Grain Yield,Quality, and Nodulation of Cowpea (Vigna unguiculata (L.) Walp.) in North-Western India

Micronutrient deficiency is a major constraint for the growth, yield and nutritional quality of cowpea which results in nutritional disorders in humans. Micronutrients including molybdenum (Mo), iron (Fe) and zinc (Zn) play a pivotal role in crop nutrition, and their role in different metabolic processes in crops has been highlighted. In order to increase the nutritional quality of cowpea, a field experiment was conducted for two years in which the effect of Mo along with iron (Fe) and zinc (Zn) on productivity, nitrogen and micronutrient uptake, root length and the number of nodules in cowpea cultivation was investigated. It was found that the foliar application of Fe and Zn and their interaction with Mo application through seed priming as well as soil application displayed increased yield, nutrient concentration, uptake and growth parameters which helped to enhance the nutritional quality of cowpea for consumption by the human population. The results of the above experiments revealed that among all the treatments, the soil application of Mo combined with the foliar application of 0.5% each of FeSO₄·7H₂O and ZnSO₄·7H₂O (M₂F₃ treatment) enhanced the grain and stover yield of cowpea, exhibiting maximum values of 1402 and 6104.7 kg ha⁻¹, respectively. Again, the M₂F₃ treatment resulted in higher Zn, Fe and Mo concentrations in the grain (17.07, 109.3 and 30.26 mg kg⁻¹, respectively) and stover (17.99, 132.7 and 31.22 mg kg⁻¹, respectively) of cowpea. Uptake of Zn, Fe and Mo by the grain (25.23, 153.3 and 42.46 g ha⁻¹, respectively) as well as the stover (104.2, 809.9 and 190.6 g ha⁻¹, respectively) was found to be maximum for the M₂F₃ treatment. The root length (30.5 cm), number of nodules per plant (73.0) and N uptake in grain and stover (55.39 and 46.15 kg ha⁻¹) were also higher for this treatment. Overall, soil application of Mo along with the foliar application of FeSO₄·7H₂O (0.5%) and ZnSO₄·7H₂O (0.5%) significantly improved yield outcomes, concentration, uptake, root length, nodules plant⁻¹ and N uptake of cowpea to alleviate the micronutrient deficiency.