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Notch receptors play a significant role in the development and the regulation of cell-fate in several multicellular organisms. For normal differentiation, genomes are essential as their regular roles and play a role in cancer is dysregulated. Notch 3 has been shown to play a major role in lung cancer function and therefore, inhibition of notch 3 protein activation represents a clear plan for cancer treatment. This study accomplished a combined structure- and ligand-based pharmacophore hypothesis to explore novel notch 3 inhibitors. The analysis identified common lead molecule ZINC000013449462 that showed better XP GScore and binding energy score than the reference inhibitor DAPT. The identified lead compound that passed all the druggable characteristics exhibited stable binding. Furthermore, the lead molecule can also form hydrogen and salt bridge interactions with binding site residues Asp1621 and Arg1465 residues, respectively of the active pockets of notch 3 protein. In essence, the inhibitory activity of the hit was validated across 109 NSCLC cell lines by employing a deep neural network algorithm. Our study proposes that ZINC000013449462 would be a possible prototype molecule towards the notch 3 target and further examined by clinical studies to combat NSCLC. [ABSTRACT FROM AUTHOR]

Tumor dissemination and relapse in lung cancer were found to be due to the existence of cancer stem cells. In particular, the β -catenin pathway is found to be one of the crucial pathways in maintaining the stem-like properties of the cells. Thus, targeting the β -catenin family of proteins is a significant therapeutic route in the treatment of lung cancer. Therefore, in the present study, a pharmacophore-based drug repurposing approach was accomplished to pinpoint potent β -catenin inhibitors from the DrugBank database. Primarily, ligand-based pharmacophore hypothesis (AAHHR) was generated using existing β -catenin inhibitors available in the literature and utilized for library screening. Subsequently, the inhibitory activity of the screened compounds was examined by the hierarchical docking process and the Prime MM-GBSA algorithm. Moreover, quantum chemical calculations and molecular dynamics simulations were executed to analyze the inhibitory effects of the screened hit molecule. The results indicate that hit molecule, DB08047 was found to possess better binding free energy, favorable ligand strain energy, satisfactory pharmacokinetic properties and superior free energy landscape profile. Eventually, the pIC 5 0 values of the lead compounds were predicted by the AutoQSAR algorithm. It is noteworthy to mention that DB08047 was found to possess pyrazole scaffolds which could downregulate β -catenin pathway and thus facilitate the controlled cell growth/inhibit tumor growth. A systematic study was undertaken to determine selective β -catenin inhibitors using integrated in-silico repurposing approach. The hit identified by our integrated approach provides high binding affinity and indicates directions for medicinal chemists to further explore indazole derivatives as potential drug candidates for the management of cancer in the near future. [ABSTRACT FROM AUTHOR]

Road traffic accidents are a major social concern as well as a crucial issue for the public in recent days due to the risk factors involved. Analysing and identifying the major risk factors of road accident is still a challenging task. In this paper, a fuzzy Context-free Grammar (FCFG)-based association rule mining (ARM) technique is proposed to categorize a heterogeneous road accident dataset into two categories based on the critical factors such as total number of accidents (TA), persons killed (PK) and persons injured (PI). The role of the fuzzy grammar in this paper is to govern the entire algorithm using the prescribed grammar rules to proceed further. The considered road accident dataset does not have class labels; hence there is a need to assign class labels for the available data instance. The accident data with assigned class labels are given as input to K-nearest neighbour (KNN) machine learning algorithm in order to train the classifier for testing purpose. Further, the collected test data from the user are utilized by the KNN classifier for carrying out the performance analysis of the proposed algorithm. The case study is conducted on the National Highway roads, India, to examine the proposed approach. The experimentations are executed for road accident records using MATLAB software and the analysis is made using the following performance measures: accuracy, recall or sensitivity, precision or specificity and F1 score. A comparative study is accomplished with existing algorithms in order to show that the proposed algorithm works with improved accuracy of more than 83%. The results suggested that the road users are responsible for the acceptance or rejection of safe or un-safe roads, respectively. [ABSTRACT FROM AUTHOR]