Graduation Year
2019
Document Type
Dissertation
Degree
Ph.D.
Degree Name
Doctor of Philosophy (Ph.D.)
Degree Granting Department
Chemistry
Major Professor
Wayne Guida, Ph.D.
Co-Major Professor
Kenyon Daniel, Ph.D.
Committee Member
Yu Chen, Ph.D.
Committee Member
David Merkler, Ph.D.
Committee Member
Wesley Brooks, Ph.D.
Keywords
Computational chemistry software, Virtual Molecular Modeling, Virtual Target Screening
Abstract
This work expounds on some of the current computational tools and programs available and the best practices associated with their use. A high-level introduction, intended for both novices and the semi-experienced, focusing on the more common programs used in scientific literature is the scope of this topic. Both classical and quantum techniques are described. Classical methodologies include Molecular Dynamics, Monte Carlo, energy minimization methods, molecular docking, low-mode, and homology modeling. Quantum chemistry techniques are also discussed encompassing Hartree-Fock, Post-Hartree-Fock theories, and Density Functional Theory along with associated basis sets.
Along with established methodologies, novel theoretical methods are introduced for furthering the application of computational modeling. Constituent partitioning consensus docking makes use of disparate docking methodologies to elucidate physical characteristics of protein binding sites. This opus also advances the function of virtual target screening, implementing robust algorithmic treatment of the protocol and improving the accuracy and scope of target identification and binding site description. The introduction focuses on theoretical approaches while subsequent chapters encompass the execution of these techniques in practical applications of drug discovery.
Scholar Commons Citation
Metcalf, Rainer, "Constituent Partitioning Consensus Docking Models and Application in Drug Discovery" (2019). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/8058
