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Novel hydrazine derivatives as selective DPP-IV inhibitors: findings from virtual screening and validation through molecular dynamics simulations

Overview of attention for article published in Journal of Molecular Modeling, April 2014
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Title
Novel hydrazine derivatives as selective DPP-IV inhibitors: findings from virtual screening and validation through molecular dynamics simulations
Published in
Journal of Molecular Modeling, April 2014
DOI 10.1007/s00894-014-2118-7
Pubmed ID
Authors

Omprakash Tanwar, Girdhar Singh Deora, Lalima Tanwar, Gautam Kumar, Sridhara Janardhan, Md. Mumtaz Alam, Md. Shaquiquzzaman, Mymoona Akhter

Abstract

The present study demonstrates and validates the discovery of two novel hydrazine derivatives as selective dipeptidyl peptidase-IV (DPP-IV) inhibitors. Virtual screening (VS) of publicly available databases was performed using virtual screening workflow (VSW) of Schrödinger software against DPP-IV and the most promising hits were selected. Selectivity was further assessed by docking the hits against homology modeled structures of DPP8 and DPP9. Two novel hydrazine derivatives were selected for further studies based on their selectivity threshold. To assess their correct binding modes and stability of their complexes with enzyme, molecular dynamic (MD) simulation studies were performed against the DPP-IV protein and the results revealed that they had a better binding affinity towards DPP-IV as compared to DPP 8 and DPP 9. The binding poses were further validated by docking these ligands with different softwares (Glide and Gold). The proposed binding modes of hydrazines were found to be similar to sitagliptine and alogliptine. Thus, the study reveals the potential of hydrazine derivatives as highly selective DPP-IV inhibitors.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 22%
Researcher 7 22%
Professor > Associate Professor 4 13%
Lecturer > Senior Lecturer 3 9%
Student > Doctoral Student 2 6%
Other 8 25%
Unknown 1 3%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 6 19%
Agricultural and Biological Sciences 6 19%
Chemistry 5 16%
Biochemistry, Genetics and Molecular Biology 4 13%
Medicine and Dentistry 4 13%
Other 2 6%
Unknown 5 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 April 2014.
All research outputs
#20,226,756
of 22,751,628 outputs
Outputs from Journal of Molecular Modeling
#624
of 811 outputs
Outputs of similar age
#193,272
of 226,111 outputs
Outputs of similar age from Journal of Molecular Modeling
#9
of 14 outputs
Altmetric has tracked 22,751,628 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 811 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.