Discovery of new oxadiazolo pyridine derivatives as potent ghrelin O-acyltransferase inhibitors using molecular modeling techniques.

Diabesity is a major global health concern, and ghrelin O-acyltransferase (GOAT) acts as an important target for the development of new inhibitors of this disease. The present work highlights a detailed QSAR study using QSARINS software, which provides an excellent model equation using descriptors. Here, the best model equation developed has two variables, namely MLFER_E and XlogP, with statistical parameters R2 = 0.8433, LOF = 0.0793, CCCtr = 0.915, Q2LOO = 0.8303, Q2LMO = 0.8275, CCCcv = 0.9081, R2ext = 0.7712, and CCCext = 0.8668. A higher correlation of the key structural fragments with activity is validated by the developed QSAR model. Furthermore, molecular docking helped us identify the binding interactions. Thirty four new molecules with better predicted biological activity (pIC50) were designed. The binding energy of four compounds have shown higher binding activity into the membrane protein (PDB Id: 6BUG). Molecular dynamics simulation has established the stability of the protein-ligand complex over 100 ns. DFT and ADME-toxicity analyses also confirmed their drug-like properties. Based on our findings, we report that these new oxadiazolo pyridine derivatives lead to the development of potent candidates for further development. Graphical abstract: METTL3-mediated HOTAIRM1 promotes vasculogenic mimicry in glioma via regulating IGFBP2 expression. METTL3 expression is high in glioma cells and tissues that stabilize and enhance HOTAIRM1 expression. This HOTAIRM1 then interacts with IGFBP2 which in turn promotes glioma cell malignancy and vasculogenic mimicry (VM) formation, thus providing a new direction for glioma therapy. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-023-00167-z.

Sentiment analysis in multilingual context: Comparative analysis of machine learning and hybrid deep learning models.

This research paper investigates the efficacy of various machine learning models, including deep learning and hybrid models, for text classification in the English and Bangla languages. The study focuses on sentiment analysis of comments from a popular Bengali e-commerce site, "DARAZ," which comprises both Bangla and translated English reviews. The primary objective of this study is to conduct a comparative analysis of various models, evaluating their efficacy in the domain of sentiment analysis. The research methodology includes implementing seven machine learning models and deep learning models, such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional 1D (Conv1D), and a combined Conv1D-LSTM. Preprocessing techniques are applied to a modified text set to enhance model accuracy. The major conclusion of the study is that Support Vector Machine (SVM) models exhibit superior performance compared to other models, achieving an accuracy of 82.56% for English text sentiment analysis and 86.43% for Bangla text sentiment analysis using the porter stemming algorithm. Additionally, the Bi-LSTM Based Model demonstrates the best performance among the deep learning models, achieving an accuracy of 78.10% for English text and 83.72% for Bangla text using porter stemming. This study signifies significant progress in natural language processing research, particularly for Bangla, by enhancing improved text classification models and methodologies. The results of this research make a significant contribution to the field of sentiment analysis and offer valuable insights for future research and practical applications.

Mononuclear Zn(II) Complex Based on N2O Ligand Compartment: First Case to Detect Nitro Explosives.

This paper scrutinises the development of low-cost hypersensitive fluorescent probes manipulated chemically with Schiff base complexes and their prospective applicability for the detection of nitro explosives in light of the rapidly expanding demand for anti-trafficking measures. In this study, a new Zn(II) metal complex has been synthesized in one pot using the Schiff base ligand L= 2-methoxy-5-methyl-N-(2-pyridin-2-ylmethylene) aniline. The complex was also thoroughly characterised using various spectroscopic tools and subjected to single crystal XRD analysis. In the asymmetric unit, square pyramidal zinc (II) centre exist in the inner N2O compartment of the ligand L. The intermolecular Cg···Cg interactions exist between two different asymmetric residual units lead to supramolecular assembly along b axis. By turning off the fluorescence response, the complex serves as a sensor for the detection of nitro aromatics in CH3CN solution. A significant quenching efficiency has been reported with a quenching constant (KSV) 1.8 × 104 M-1 for 4-nitrobenzoic acid during investigation of sensing phenomenon in solution phase. In addition, determining the binding stoichiometry of the chemosensor with NO2 and the binding constant, the mechanism of fluorescence quenching has also been postulated. The detection limit of NO2 is 7.6×10 -7 M, with the binding constant k = 1.1021× 108 M-1. Additionally, the DFT calculation makes it easier to comprehend the appropriate binding process in light of the findings of experiments. We also designed a paper sensor strip for the visual detection of Nitro Explosive Residues in light of the sensor's potential used in forensic investigations.

BTSD: A curated transformation of sentence dataset for text classification in Bangla language.

The Bangla Transformation of Sentence Classification dataset addresses the resource gap in natural language processing (NLP) for the Bangla language by providing a curated resource for Bangla sentence classification. With 3,793 annotated sentences, the dataset focuses on categorizing Bangla sentences into Simple, Complex, and Compound classes. It serves as a benchmark for evaluating NLP models on Bangla sentence classification, promoting linguistic diversity and inclusive language models. Collected from publicly accessible Facebook pages, the dataset ensures balanced representation across the categories. Preprocessing steps, including anonymization and duplicate removal, were applied. Three native Bangla speakers independently assessed the Transformation of Sentence labels, enhancing the dataset's reliability. The dataset empowers researchers, practitioners, and developers to build accurate and robust NLP models tailored to the Bangla language. It offers insights into Bangla syntax and structure, benefiting linguistic research. The dataset can be used to train models, uncover patterns in Bangla language usage, and develop effective NLP applications across domains.

Computational insights into pediatric adenovirus inhibitors: in silico strategies for drug repurposing.

Human adenovirus (HADV) infection can pose a serious threat to children, leading to a variety of respiratory illnesses and other complications. Particularly, children with weak immune systems are vulnerable to severe adenovirus infections with high mortality. The main focus of this study is to propose new antiviral agents as lead HADV inhibitors for children. So, several antiviral agents used in children were subjected to finding new HADV inhibitors using important computational methods of molecular docking, molecular dynamics (MD) simulation, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding free energy calculations, density functional theory (DFT), and pharmacokinetic analysis. Molecular docking of standard cidofovir along with other ligands, suggested that sofosbuvir has the highest binding energy (-10.8 kcal/mol), followed by baloxavir marboxil (-10.36 kcal/mol). Further, the analysis of molecular interactions using MD simulation (100 ns) and MM-PBSA indicated that baloxavir marboxil has formed the most stable protein-ligand complex with HADV, followed by sofosbuvir. The binding free energies of baloxavir marboxil and sofosbuvir were found to be -61.724 kJ/mol and -48.123 kJ/mol, respectively. The DFT and drug-likeness properties of these compounds were also investigated. Overall, two antiviral agents, such as baloxavir marboxil, and sofosbuvir are suggested as lead repurposed candidates against HADV.Communicated by Ramaswamy H. Sarma.

Molecular iodine catalyzed C(sp2)-H sulfenylation of biologically active enaminone compounds under mechanochemical conditions and studies on their biocidal activity including molecular docking and DFT.

Here we demonstrated a solvent free, mechanochemical I2 catalyzed C(sp2)-H sulfenylation of enaminones under grinding condition. Only catalytic amount of I2 is required on silica surface without any external heating. The reaction time has reduced to a great extent in comparison to their solution based counterpart. The frictional energy created by ball-mill on mesoporous silica materials has attracted much attention towards this mechanochemical approach for molecular heterogeneous catalysis. Their large surface area and well defined porous architecture certainly increase the catalytic ability of iodine in this developed protocol. Anti-microbial activities of our synthesized compounds were investigated against two gram positive (Staphylococcus aureus and Bacillus cereus) and two gram negative (Escherichia coli and Klebsiella pneumonia) bacteria. To understand the potency of these compounds (3a-3m) as antimalarial agents, molecular docking studies were also performed. Density functional theory was also used to investigate the chemical reactivity and kinetic stability of the compound 3a-3m.

Repurposing of anti-lung cancer drugs as multi-target inhibitors of SARS-CoV-2 proteins: An insight from molecular docking and MD-simulation study.

Herein we have selected seventeen anti-lung cancer drugs to screen against Mpro, PLpro and spike glycoproteins of SARS-CoV-2to ascertain the potential therapeutic agent against COVID-19. ADMET profiling were employed to evaluate their pharmacokinetic properties. Molecular docking studies revealed that Capmatinib (CAP) showed highest binding affinity against the selected proteins of SARS-CoV-2. Molecular Dynamics (MD) simulation and the analysis of RMSD, RMSF, and binding energy confirmed the abrupt conformational changes of the proteins due to the presence of this drug. These findings provide an opportunity for doing advanced experimental research to evaluate the potential drug to combat COVID-19.

Fuchs Endothelial Corneal Dystrophy associated risk variant, rs3768617 in LAMC1 shows allele specific binding of GFI1B.

AIMS: To investigate the genetic and functional association of an intronic variant of LAMC1, rs3768617 with Fuchs endothelial corneal dystrophy (FECD) in the Indian population. METHODS: Blood samples were collected from age and sex matched 356 controls and 120 FECD patients after a detailed assessment via specular microscopy. Genomic DNA was extracted and genotyping was done by fluorescence based capillary electrophoresis. The genetic association of rs3768617 polymorphisms was computed by the chi-square (χ2) test. Bioinformatics studies were performed to find the allele specific binding of different transcription factors in the region of rs3768617 and functional evaluation assessed by luciferase assay followed by Electrophoretic Mobility Shift Assay (EMSA) and Chromatin Immunoprecipitation assay (ChIP). Immunofluorescence assay was carried out to check for any differential expression of GFI1B between control and FECD endothelium samples. RESULTS: SNP rs3768617 {chr1:183123365 (GRCh38.p13)} was found to be genetically associated with FECD in Indian population (p = 2.646 × 10-8). Luciferase assay suggested that the rs3768617 locus has a regulatory role. In silico analysis showed that the transcription factor, GFI1B binds to the risk allele 'G' of rs3768617, but not to the protective allele 'A' which was also experimentally validated by EMSA. High enrichment of DNA flanking the surrounding region of rs3768617 was also found in presence of GFI1B specific antibody in ChIP assay. There was a 0.63 fold decrease in GFI1B expression in FECD affected corneal endothelium compared to control endothelium. CONCLUSIONS: The genetic association of rs3768617 in LAMC1 with FECD pathogenesis is mediated by GFI1B, thus finding the functional role of LAMC1 in FECD pathogenesis.

Screening of Drug Efficacy of Rosmarinic Acid Derivatives as Aurora Kinase Inhibitors by Computer-Aided Drug Design Method.

BACKGROUND: Aurora kinases (AKs) belong to the serine/threonine kinase family and play a crucial role in regulating the cell cycle. Therefore, AKs are the hopeful target for anticancer therapies and these findings have encouraged researchers to rigorously hunt small molecule aurora kinase inhibitors, not only for research articles but also for use as therapeutic agents. OBJECTIVE: The present study helps us to identify and screen the best phytochemicals as potent inhibitors against AKs. These potent inhibitors come from the various substitution of rosmarinic acid (RA). METHODS: In this paper, we choose different tested derivative compounds for designing anticancer drugs by substituting various functional groups of standard drug RA. In silico studies were carried out to appreciate better drug candidature of some of these derivative compounds. This study was performed on 56 derived compounds of the standard RA. DFT study was conducted using the UB3LYP/6-311++G(d,p) basis set to study HOMO-LUMO energies, dipole moments, using the Gaussian16 suite. Some of the derived parameters, like ionization potential, electron affinity, softness- hardness, chemical potential, and electrophilicity index were noted. A docking study was performed with AKs inhibiting receptor using AutoDock 4.2. ADME prediction was made with the preADMET web tool. Molecular descriptor properties were predicted with molinspiration and OSIRIS property explorer. RESULTS: Out of the 56 derivatives, 11 have passed all the rules of drug candidature, to serve as best AKs inhibitor, in a theoretical manner. CONCLUSION: This study should be supported by a new proposal to explore future studies with these 11 compounds against cancer.