Purdue University Graduate School
2023_Prageeth_Wijewardhane_PhD_Thesis_Final_Final_deposited.pdf (38.37 MB)


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posted on 2023-04-25, 23:10 authored by Godakande Kankanamge P WijewardhaneGodakande Kankanamge P Wijewardhane

Drug discovery and development has experienced a tremendous growth in the recent

years, and methods to accelerate the process are necessary as the demand for effective drugs

to treat a wide range of diseases continue to increase. Nevertheless, the majority of conventional

techniques are labor-intensive or have relatively low yields. As a result, academia

and the pharmaceutical industry are continuously seeking for rapid and efficient methods to

accelerate the drug discovery pipeline. Therefore, in order to expedite the drug discovery

process, recent developments in physical and artificial intelligence models have been utilized

extensively. However, the overarching problem is how to use these cutting-edge advancements

in artificial intelligence to enhance drug discovery? Therefore, this dissertation work

focused on developing and applying artificial intelligence and physical models to accelerate

the drug discovery pipeline at different stages. As the first study reported in the dissertation,

the potential to apply graph neural network-based machine learning architectures

with the assistance of molecular modeling features to identify plausible drug leads out of

structurally similar chemical databases is assessed. Then, the capability of applying molecular

modeling methods including molecular docking and molecular dynamics simulations to

identify prospective targets and biological pathways for small molecular drugs is discussed

and evaluated in the following chapter. Further, the capability of applying state-of-the-art

deep learning architectures such as multi-layer perceptron and recurrent neural networks

to optimize the formulation development stage has been assessed. Moreover, this dissertation

has contributed to assist functionality identification of unknown compounds using

simple machine learning based computational frameworks. The developed omics data analysis

pipeline is then discussed in order to comprehend the effects of a particular treatment

on the proteome and lipidome levels of cells. In conclusion, the potential for developing and

utilizing various artificial intelligence-based approaches to accelerate the drug discovery and

development process is explored in this research. Thus, these collaborative studies intend

to contribute to ongoing acceleration efforts and advancements in the drug discovery and

development field.


Degree Type

  • Doctor of Philosophy


  • Chemistry

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Gaurav Chopra

Additional Committee Member 2

Graham Cooks

Additional Committee Member 3

Herman O. Sintim

Additional Committee Member 4

Ming Chen