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In-silico Methods for Drug Discovery: Applications of Molecular Dynamics, Drug Docking, and Machine Learning
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- Author / Creator
- Jaundoo, Rajeev
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Drug discovery is a venture that is costly in both time and money. In-silico methods are a core part of biomedical research, from traditional tools such as drug docking and molecular dynamics to newer machine learning frameworks, all of which are more efficient in both time and cost compared to wholly experimental approaches. This thesis highlights 3 separate studies that reflect the past, present, and future of in-silico research, starting with the development of novel platelet activated ligands for the purpose of targeted drug delivery using traditional tools such as drug docking and simulated annealing. The second study demonstrated how machine learning was used for classification of drug activity (agonist, antagonist, or non-binder) towards a group of macromolecules all within the nuclear receptor family. Finally, the third study used machine learning in a regression task to predict bioelectric potentials and ion channel activity of a cellular network to serve as a replacement for another in-silico application, reducing the computational resources required for these predictions and providing the ability to scale to larger cell networks with ease. Development of newer, more advanced in-silico tools increases the accuracy of drug treatment predictions, leading to more effective therapeutics and lower rates of failure during pre-clinical as well as clinical phases. Not only that, but computational methods excel at drug repurposing, a process in which existing pharmaceuticals are used for indications not originally intended. Bioinformatic techniques and machine learning take advantage of the substantial amount of biological, pharmacological, etc. data available to identify adverse and beneficial interactions that would otherwise be missed. Overall, in-silico techniques are performed in tandem with in-vitro and in-vivo experiments, used to validate computational predictions, during various phases of drug discovery and repurposing, making them a core part of biomedical research at large.
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- Subjects / Keywords
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- Graduation date
- Spring 2024
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.