Dipeptidyl Peptidase-IV (DPP-IV) was found to be one of the emerging targets in the field of antidiabetic drug development. In recent years, Piperidone analogs have shown their application in the field of diabetology as observed in the crystallographic structure of DPP-IV. All the ligand molecules were designed considering the pharmacophoric features of the GGO901 (Co-crystallised ligand of DPP-IV with the PDB entry-2OQI). It was thought worthwhile to perform docking study by extracting the crystallographic structure of Dipeptidyl Peptidase-IV inhibitor with all the virtually developed molecules to ensure the better binding energy which would be well able to explain the ligand target interaction of novel piperidone analogs, actively inhibiting DPP-IV enzyme. Based on the docking output, a few novel imidazole linked piperidone analogs were synthesized & as a part of the structure elucidation process various spectroscopic data were collected and subsequently analysed. All the analytically proven synthesized compounds were further sent for biological evaluation.

This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder-decoder network, meta-learning, conditional variational encoder-decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.