Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Identifying potential therapeutic molecules for hepatocellular carcinoma through machine learning-based drug repurposing
Download
Tugce_Baser_PhD_Thesis.pdf
Date
2024-9-06
Author
Başer, Tuğçe
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
49
views
33
downloads
Cite This
Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a high mortality rate due to limited treatment options. Systemic drug treatments increase patient survival and often extend life by several months. The development of new small molecule chemotherapeutics is both time consuming and costly. Therefore, drug repurposing is being used as an effective strategy to identify and implement new treatment options for this mortal disease. The aim of this study is to identify potential drug candidates for the treatment of HCC through reuse of existing compounds using the machine learning tool MDeePred. The open target platform, UniProt, ChEMBL, and Expasy databases were used to create a dataset for MDeePred to predict drug- target interactions (DTIs). Enrichment analyses of DTIs were conducted, leading to the selection of 6 out of 380 DTIs identified by MDeePred for further analyses. The physicochemical properties, lipophilicity, water solubility, drug-likeness and medicinal chemistry properties of the drug candidates and approved drugs for advanced stage HCC (lenvatinib, regorafenib, and sorafenib) were meticulously and curated. The majority of drug candidates fell within conventional ranges in terms of drug properties and demonstrated target docking abilities. Our findings revealed the binding efficiency of selected drug compounds to identified targets associated with HCC. As a result, small molecules were identified that can be further evaluated experimentally as potential drug candidates in HCC. This study also highlights the importance of the MDeePred deep learning tool in in silico drug repurposing studies in cancer treatment.
Subject Keywords
Drug candidate
,
Drug repurposing
,
Hepatocellular carcinoma,
,
Machine learning
,
MDeePred
URI
https://hdl.handle.net/11511/111369
Collections
Graduate School of Informatics, Thesis
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
T. Başer, “Identifying potential therapeutic molecules for hepatocellular carcinoma through machine learning-based drug repurposing,” Ph.D. - Doctoral Program, Middle East Technical University, 2024.