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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.
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