Abstract. Green synthesis of nano MgO particles and their application in the formylationof isocyanates of N-Fmoc/Cbz/Boc protected amino acids were reported. Nano magnesium oxide catalysed reaction of isocyanate with 96% formic acid was established to obtain formamides. For this, the carboxyl group of protected amino acids was activated via mixed anhydride method and treated with NaN3. The formed azides were converted into their isocyanates through Curtius rearrangement and treated with HCOOH and catalytic amount of nano MgO. The advantages of this method were being remarkably simple and attaining economically low-cost nano metal oxide under milder reaction conditions. Most importantly, MgO could be easily separated and other basic impurities were removed through a simple work-up. This protocol showed high e ciency in catalysing the transformation in a greener fashion. The molecular docking study of the synthesized compounds was performed against the macromolecules sortase-A and glucosamine 6-phosphate synthase to understand the binding interactions. The results of in vitro antibacterial activities of the synthesized compounds were supported by docking analysis.

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Quorum sensing (QS) is a bacterial communication using signal molecules, by which they sense population density of their own species, leading to group behavior such as biofilm formation and virulence. Autoinducer-2 (AI2) is a QS signal molecule universally used by both gram-positive and gram-negative bacteria. Inhibition of QS mediated by AI2 is important for various practical applications, including prevention of gum-disease caused by biofilm formation of oral bacteria. In this research, molecular docking and molecular dynamics (MD) simulations were performed for molecules that are chemically similar to known AI2 inhibitors that might have a potential to be quorum sensing inhibitors. The molecules that form stable complexes with the AI2 receptor protein were found, suggesting that they could be developed as a novel AI2 inhibitors after further in vitro validation. The result suggests that combination of ligand-based drug design and computational methods such as MD simulation, and experimental verification, may lead to development of novel AI inhibitor, with a broad range of practical applications.

Janus kinase 2 (JAK2) is emerging as a potential therapeutic target for many inflammatory diseases such as myeloproliferative disorders (MPD), cancer and rheumatoid arthritis (RA). In this study, we have collected experimental data of JAK2 protein containing 6021 unique inhibitors. We then characterized them based on Morgan (ECFP6) fingerprints followed by clustering into training and test set based on their molecular scaffolds. These data were used to build the classification models with various supervised machine learning (ML) algorithms that could prioritize novel inhibitors for future drug development against JAK2 protein. The best model built by Random Forest (RF) and Morgan fingerprints achieved the G‐mean value of 0.84 on the external test set. As an application of our classification model, virtual screening was performed against Drugbank molecules in order to identify the potential inhibitors based on the confidence score by RF model. Nine potential molecules were identified, which were further subject to molecular docking studies to evaluate the virtual screening results of the best RF model. This proposed method can prove useful for developing novel target‐specific JAK2 inhibitors.

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