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Saw, Simeon. Cheminformatics investigation of kinase inhibition, cytotoxicity and volume of distribution data. Doctoral Degraee(Bioscience). Kasetsart University. Office of the University Library. : Kasetsart University, 2019.
Cheminformatics investigation of kinase inhibition, cytotoxicity and volume of distribution data
Name: Saw, Simeon
Classification :.LCCS: QP606.P76
LCSH:
Cheminformatics
LCSH:
Epidermal growth factor
LCSH:
Protein kinases
-- Inhibitors
LCSH:
Carcinogenesis
Abstract:
Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. Structure-based quantum mechanical (QM) quantitative structure activity relationship models (QSAR) is generated to facilitate structure guided optimization or protein kinase inhibitors. Additional QSAR models were generated using alignment independent physicochemical, e-state and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test set (R2 ~0.7) and was demonstrably more predictive than the QSAR models built using other methods. Analysis of the descriptors coefficients from the former methods as allowed for a detailed rationalization of the active site SAR which has implications for subsequent design iterations. Volume of distribution (Vdss) is a measure of how effectively a drug molecule is distributed throughout the body and along with the clearance, it determines the halflife and therefore the drug dosing interval. A number of different pre-clinical approaches are available to predict the Vdss in human including quantitative structure activity relationship (QSAR) models. To date, Vdss QSAR models have not been reported for important pre-clinical species including dog, mouse and monkey. Vdss QSAR on human and the most commonly used pre-clinical species were generated, each of which differs in terms of size, chemical diversity and data quality. Intrinsic variability of the experimental Vdss data are assessed (R2 ~0.9) and compared this to experimental interspecies coefficient of correlation (R2 ~0.7) and to the in-silico models (R2 ~0.5).
Kasetsart University. Office of the University Library
Address:
Bangkok
Email:
tdckulib@ku.ac.th
Name: Matthew Paul Gleeson
Role:
Thesis Advisor
Name: Chak Sangma
Role:
Thesis CO-Advisor
Name: Patchreenart Saparpakorn
Role:
Thesis CO-Advisor
Created:
2019
Modified:
2025-06-30
Issued:
2025-06-30
วิทยานิพนธ์/Thesis
application/pdf
URL: https://www.lib.ku.ac.th/KUthesis/2562/saw-sim-all.pdf
CallNumber:
QP606.P76 .S29
eng
DegreeName: Doctor of Philosophy
Level: Doctoral Degraee
Descipline: Bioscience
Grantor: Kasetsart University
©copyrights Kasetsart University
RightsAccess:
ใช้เวลา
0.038102 วินาที
Saw, Simeon
| Title | Contributor | Type |
|---|---|---|
| Cheminformatics investigation of kinase inhibition, cytotoxicity and volume of distribution data
มหาวิทยาลัยเกษตรศาสตร์ Saw, Simeon | Matthew Paul Gleeson | วิทยานิพนธ์/Thesis |
Matthew Paul Gleeson
| Title | Creator | Type and Date Create |
|---|---|---|
| QM/MM calculations to investigate the reaction mechanisms of serine hydroxymethyltransferase (SHMT)
มหาวิทยาลัยเกษตรศาสตร์ Matthew Paul Gleeson;Patchreenart Saparpakorn | Jirapat Santatiwongchai | วิทยานิพนธ์/Thesis |
| Design, synthesis and biological evaluation of novel PDE5 protein inhibitors
สถาบันเทคโนโลยีพระจอมเกล้าเจ้าคุณทหารลาดกระบัง Matthew Paul Gleeson;Kanokthip Boonyarattanakalin | Thanachon Somnarin | วิทยานิพนธ์/Thesis |
| Cheminformatics investigation of kinase inhibition, cytotoxicity and volume of distribution data
มหาวิทยาลัยเกษตรศาสตร์ Matthew Paul Gleeson | Saw, Simeon | วิทยานิพนธ์/Thesis |
| Design, synthesis, biological and computational evaluation of novel 4-aryl-N-phenylpyrimidin-2-amines and N2, N4 - diarylpyrimidine-2,4- diamine inhibitors of protein kinases
มหาวิทยาลัยเกษตรศาสตร์ Matthew Paul Gleeson | Borvornwat Toviwek | วิทยานิพนธ์/Thesis |
Chak Sangma
Patchreenart Saparpakorn