Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83241
Title: Implementation of machine learning and other simulation protocols for the representation of G-Quadruplex stabilizer
Authors: Chiew, Kang Jing
Keywords: Science::Chemistry
Issue Date: 2019
Source: Chiew, K. J. (2019). Implementation of machine learning and other simulation protocols for the representation of G-Quadruplex stabilizer. Master's thesis, Nanyang Technological University, Singapore.
Abstract: Human chromosomal telomeres are capable of forming G-Quadruplex structures, which can inhibit the activity of telomerase, an enzyme commonly found in cancerous cells and largely responsible for their immortality. The inhibition may therefore lead to cell apoptosis in these cancer cells. As such, there have been growing interest in the research for G-Quadruplex stabilizers. This thesis focusses on the exploration and development of new representations for the structure-function relationship of G-Quadruplex-ligand biomolecular system using machine learning (ML) techniques. Two main models, namely Element Specific Persistent Homology (ESPH) and Rigidity Index-Score (RI-Score), were adapted due to their successes in representing other biomolecular systems. It was discovered that both methodologies similarly presented strong average positive correlation for the representation of the studied system. (Pearson Correlation: 0.6770-0.7871, RMSE: 0.4621-0.5811) In addition, most of the models developed performed admirably when compared to the well-established Quantitative Structure-Activity Relationship (QSAR) method. In particular, Ligand-based model using ESPH firmly outperformed all other models, new and existing alike. (Pearson Correlation: 0.7614-0.7871, RMSE: 0.4621-0.4866)
URI: https://hdl.handle.net/10356/83241
http://hdl.handle.net/10220/50085
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Theses

Files in This Item:
File Description SizeFormat 
Final Thesis Chiew Kangjing.pdf3.38 MBAdobe PDFThumbnail
View/Open

Page view(s) 50

90
checked on Oct 26, 2020

Download(s) 50

20
checked on Oct 26, 2020

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.