Automated Author ProfileKumar, Vijay
Laboratory of Organic and Medicinal Chemistry, Department of Chemistry, Central University of Punjab, Ghudda, Bathinda (Punjab)- 151401
Kumar, Vijay
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.8 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Alzheimer’s disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD and multi-target directed ligands (MTDLs) are being explored as an alternative strategy. Cholinesterase and monoamine oxidase enzymes are reported to play crucial role in the pathology of AD and multipotent ligands targeting these two enzymes simultaneously, are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for the identification of novel therapeutics. The current research work is focused on the development of potential multitarget directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing structure-based virtual screening (SBDD) approach. The ASINEX database was screened after applying pan assay interference and drug likeness filter to identify novel molecules using three docking precision criteria Highthroughput virtual screening (HTVS), Standard Precision (SP), and extra precision (XP). Additionally, binding free energy calculations, ADME and molecular dynamic simulations were also employed to get structural insights into mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified which displayed binding score of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.
Authors
- , Kailash ;
- Devi, Bharti ;
- Ashrulochan Sahoo ;
- Kumar, Vijay ;
- Dwivedi, Ashish Ranjan ;
- Thareja, Suresh ;
- Rajnish Kumar ;
- Kumar, Vinod
Alzheimer’s disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD and multi-target directed ligands (MTDLs) are being explored as an alternative strategy. Cholinesterase and monoamine oxidase enzymes are reported to play crucial role in the pathology of AD and multipotent ligands targeting these two enzymes simultaneously, are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for the identification of novel therapeutics. The current research work is focused on the development of potential multitarget directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing structure-based virtual screening (SBDD) approach. The ASINEX database was screened after applying pan assay interference and drug likeness filter to identify novel molecules using three docking precision criteria Highthroughput virtual screening (HTVS), Standard Precision (SP), and extra precision (XP). Additionally, binding free energy calculations, ADME and molecular dynamic simulations were also employed to get structural insights into mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified which displayed binding score of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.
Authors
- , Kailash ;
- Devi, Bharti ;
- Ashrulochan Sahoo ;
- Kumar, Vijay ;
- Dwivedi, Ashish Ranjan ;
- Thareja, Suresh ;
- Rajnish Kumar ;
- Kumar, Vinod
Alzheimer’s disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD and multi-target directed ligands (MTDLs) are being explored as an alternative strategy. Cholinesterase and monoamine oxidase enzymes are reported to play crucial role in the pathology of AD and multipotent ligands targeting these two enzymes simultaneously, are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for the identification of novel therapeutics. The current research work is focused on the development of potential multitarget directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing structure-based virtual screening (SBDD) approach. The ASINEX database was screened after applying pan assay interference and drug likeness filter to identify novel molecules using three docking precision criteria Highthroughput virtual screening (HTVS), Standard Precision (SP), and extra precision (XP). Additionally, binding free energy calculations, ADME and molecular dynamic simulations were also employed to get structural insights into mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified which displayed binding score of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.
Authors
- , Kailash ;
- Devi, Bharti ;
- Ashrulochan Sahoo ;
- Kumar, Vijay ;
- Dwivedi, Ashish Ranjan ;
- Thareja, Suresh ;
- Rajnish Kumar ;
- Kumar, Vinod