Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors
<b>Background:</b> Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. <b>Meth...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Pharmaceuticals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8247/18/1/13 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587721962422272 |
|---|---|
| author | George Nicolae Daniel Ion George Mihai Nitulescu Dragos Paul Mihai |
| author_facet | George Nicolae Daniel Ion George Mihai Nitulescu Dragos Paul Mihai |
| author_sort | George Nicolae Daniel Ion |
| collection | DOAJ |
| description | <b>Background:</b> Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. <b>Methods:</b> This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations. Using this pipeline, we analyzed 4680 investigational and approved drugs from DrugBank database. <b>Results:</b> The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. The candidates demonstrated strong binding energies, key molecular interactions with critical residues (e.g., Phe88, Glu161), and stable MD trajectories, particularly saredutant, a neurokinin-2 (NK2) antagonist. <b>Conclusions:</b> Beyond identifying potential AurB inhibitors, this study highlights an integrated methodology that can be applied to other challenging drug targets. |
| format | Article |
| id | doaj-art-898da08c786249ffb55eae016c070290 |
| institution | Kabale University |
| issn | 1424-8247 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj-art-898da08c786249ffb55eae016c0702902025-01-24T13:44:59ZengMDPI AGPharmaceuticals1424-82472024-12-011811310.3390/ph18010013Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B InhibitorsGeorge Nicolae Daniel Ion0George Mihai Nitulescu1Dragos Paul Mihai2Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, RomaniaFaculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, RomaniaFaculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania<b>Background:</b> Aurora kinase B (AurB) is a pivotal regulator of mitosis, making it a compelling target for cancer therapy. Despite significant advances in protein kinase inhibitor development, there are currently no AurB inhibitors readily available for therapeutic use. <b>Methods:</b> This study introduces a machine learning-assisted drug repurposing framework integrating quantitative structure-activity relationship (QSAR) modeling, molecular fingerprints-based classification, molecular docking, and molecular dynamics (MD) simulations. Using this pipeline, we analyzed 4680 investigational and approved drugs from DrugBank database. <b>Results:</b> The machine learning models trained for drug repurposing showed satisfying performance and yielded the identification of saredutant, montelukast, and canertinib as potential AurB inhibitors. The candidates demonstrated strong binding energies, key molecular interactions with critical residues (e.g., Phe88, Glu161), and stable MD trajectories, particularly saredutant, a neurokinin-2 (NK2) antagonist. <b>Conclusions:</b> Beyond identifying potential AurB inhibitors, this study highlights an integrated methodology that can be applied to other challenging drug targets.https://www.mdpi.com/1424-8247/18/1/13AURKBcancer therapyprotein kinase inhibitionvirtual screeningcomputer-aided drug design and discoveryinteraction fingerprints |
| spellingShingle | George Nicolae Daniel Ion George Mihai Nitulescu Dragos Paul Mihai Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors Pharmaceuticals AURKB cancer therapy protein kinase inhibition virtual screening computer-aided drug design and discovery interaction fingerprints |
| title | Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors |
| title_full | Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors |
| title_fullStr | Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors |
| title_full_unstemmed | Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors |
| title_short | Machine Learning-Assisted Drug Repurposing Framework for Discovery of Aurora Kinase B Inhibitors |
| title_sort | machine learning assisted drug repurposing framework for discovery of aurora kinase b inhibitors |
| topic | AURKB cancer therapy protein kinase inhibition virtual screening computer-aided drug design and discovery interaction fingerprints |
| url | https://www.mdpi.com/1424-8247/18/1/13 |
| work_keys_str_mv | AT georgenicolaedanielion machinelearningassisteddrugrepurposingframeworkfordiscoveryofaurorakinasebinhibitors AT georgemihainitulescu machinelearningassisteddrugrepurposingframeworkfordiscoveryofaurorakinasebinhibitors AT dragospaulmihai machinelearningassisteddrugrepurposingframeworkfordiscoveryofaurorakinasebinhibitors |