Molecular modelling and machine learning for the investigation of 2-oxazolidinone ribosomal antibacterials
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Description
This thesis by publication compares computational methods to analyze an untested dataset of oxazolidinones, a type of antibacterials that target ribosomes. The study evaluates structure-based techniques like molecular docking to understand their interactions, in addition to exploring ligand-based approaches, including machine learning, to create a predictive model for designing new antibacterials.
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| ID Code: | 242134 |
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| Item Type: | QUT Thesis (Master of Philosophy by Publication) |
| Supervisor: | Gandhi, Neha S. & Fairfull-Smith, Kathryn |
| Keywords: | Molecular docking, machine learning, oxazolidinone, linezolid, RNA, ribosome |
| DOI: | 10.5204/thesis.eprints.242134 |
| Pure ID: | 141352646 |
| Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Chemistry & Physics |
| Institution: | Queensland University of Technology |
| Deposited On: | 04 Aug 2023 06:15 |
| Last Modified: | 04 Aug 2023 06:15 |
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