<p>Drug discovery and development has experienced a tremendous growth in the recent</p>
<p>years, and methods to accelerate the process are necessary as the demand for effective drugs</p>
<p>to treat a wide range of diseases continue to increase. Nevertheless, the majority of conventional</p>
<p>techniques are labor-intensive or have relatively low yields. As a result, academia</p>
<p>and the pharmaceutical industry are continuously seeking for rapid and efficient methods to</p>
<p>accelerate the drug discovery pipeline. Therefore, in order to expedite the drug discovery</p>
<p>process, recent developments in physical and artificial intelligence models have been utilized</p>
<p>extensively. However, the overarching problem is how to use these cutting-edge advancements</p>
<p>in artificial intelligence to enhance drug discovery? Therefore, this dissertation work</p>
<p>focused on developing and applying artificial intelligence and physical models to accelerate</p>
<p>the drug discovery pipeline at different stages. As the first study reported in the dissertation,</p>
<p>the potential to apply graph neural network-based machine learning architectures</p>
<p>with the assistance of molecular modeling features to identify plausible drug leads out of</p>
<p>structurally similar chemical databases is assessed. Then, the capability of applying molecular</p>
<p>modeling methods including molecular docking and molecular dynamics simulations to</p>
<p>identify prospective targets and biological pathways for small molecular drugs is discussed</p>
<p>and evaluated in the following chapter. Further, the capability of applying state-of-the-art</p>
<p>deep learning architectures such as multi-layer perceptron and recurrent neural networks</p>
<p>to optimize the formulation development stage has been assessed. Moreover, this dissertation</p>
<p>has contributed to assist functionality identification of unknown compounds using</p>
<p>simple machine learning based computational frameworks. The developed omics data analysis</p>
<p>pipeline is then discussed in order to comprehend the effects of a particular treatment</p>
<p>on the proteome and lipidome levels of cells. In conclusion, the potential for developing and</p>
<p>utilizing various artificial intelligence-based approaches to accelerate the drug discovery and</p>
<p>development process is explored in this research. Thus, these collaborative studies intend</p>
<p>to contribute to ongoing acceleration efforts and advancements in the drug discovery and</p>
<p>development field.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22689505 |
| Date | 25 April 2023 |
| Creators | Godakande Kankanamge P Wijewardhane (15350731) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ACCELERATING_DRUG_DISCOVERY_AND_DEVELOPMENT_USING_ARTIFICIAL_INTELLIGENCE_AND_PHYSICAL_MODELS/22689505 |
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