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Development and Test of New Technologies in Ligand and Structure-based Drug Design

Han, Fengyang (2024) Development and Test of New Technologies in Ligand and Structure-based Drug Design. Master's Thesis, University of Pittsburgh. (Unpublished)

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In the rapidly evolving field of drug discovery, the application of computer-aided Ligand-Based Drug Design (LBDD) and Structure-Based Drug Design (SBDD) methodologies has become increasingly crucial. Despite advancements in the field, certain challenges persist that hinder progress, particularly considering the vast conformational space of drug molecules in virtual screening, which needs to be constrained to expedite docking. Furthermore, the issue of drug resistance is gaining more attention, with a pressing need for the discovery of new antibiotics to mitigate a potential crisis of drug unavailability. This thesis begins by exploring the development of molecular energy prediction tools, which were developed on advanced Deep Learning (DL) methods to predict the energy of active conformations within the PDB database. We then statistically calculated the energy differences between all active conformations and the lowest energy conformation, providing statistics and threshold references for eliminating high-energy conformations in virtual screening, paving the way for a more efficient and targeted drug discovery process. Another focus is the use of external electric fields (EEF) in biased MD simulations, in combination with positively charged Bovine pancreas trypsin inhibitor (BPTI), to explore the open conformation of the E. coli Mechano Sensitive Channel protein with Large conductance (Ec-MscL), which was a challenging task with traditional experimental and computational methods. This research also employs virtual screening and docking methods to identify potential hits that may hinder the closing of the Ec-MscL channel. Furthermore, it delves into the complexities of ligand and protein channel interactions using conventional molecular dynamics simulations, offering insights for further experiment-based drug development.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Han, Fengyangfeh34@pitt.edufeh34
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWang, Junmeijuw79@pitt.edujuw79
Committee MemberFeng, Zhiweizhf11@pitt.eduzhf11
Committee MemberKoes, Daviddkoes@pitt.edudkoes
Date: 25 April 2024
Date Type: Publication
Defense Date: 10 April 2024
Approval Date: 25 April 2024
Submission Date: 25 April 2024
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 61
Institution: University of Pittsburgh
Schools and Programs: School of Pharmacy > Pharmaceutical Sciences
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Structure based drug design; Ligand based drug Design; Machine learning
Date Deposited: 25 Apr 2024 18:16
Last Modified: 25 Apr 2024 18:16


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