Automated Author ProfileHassan, Walid M. I.
Hassan, Walid M. I.
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: 1.3 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to −11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended. Communicated by Ramaswamy H. Sarma
Authors
- Islam, Md Rashedul ;
- Osman, Osman I. ;
- Hassan, Walid M. I.
Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to −11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended. Communicated by Ramaswamy H. Sarma
Authors
- Islam, Md Rashedul ;
- Osman, Osman I. ;
- Hassan, Walid M. I.