Thesis
Machine learning models in drug discovery and development
Master of Science (M.S.), Drexel University
May 2023
DOI:
https://doi.org/10.17918/00001785
Abstract
Machine learning models have seen increased use in drug discovery and development. With the rise in their popularity, it has become important to describe machine learning in a manner that is easily understandable to a researcher not well-versed in machine learning. This thesis introduces machine learning to such researchers, with particular focus on supervised machine learning models that learn to make predictions about a target concept from training data. A general overview of the machine learning methods and the molecular data representations are provided, and two case studies are discussed to demonstrate some of the common applications: drug-target interaction prediction using artificial neural networks, and drug property prediction using decision trees.
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Details
- Title
- Machine learning models in drug discovery and development
- Creators
- Rohan Gupta
- Contributors
- Ahmet Sacan (Advisor)
- Awarding Institution
- Drexel University
- Degree Awarded
- Master of Science (M.S.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Number of pages
- viii, 58 pages
- Resource Type
- Thesis
- Language
- English
- Academic Unit
- College of Medicine; Pharmacology and Physiology; Drexel University
- Other Identifier
- 991021212314304721