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Molecular Simulations and Binding Free Energy Calculations for Drug Discovery
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Molecular Simulations and Binding Free Energy Calculations for Drug Discovery. |
| 개인저자 | Lim, Nathan M. |
| 단체저자명 | University of California, Irvine. Pharmacological Sciences - Ph.D.. |
| 발행사항 | [S.l.]: University of California, Irvine., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 236 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781687962904 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Mobley, David L. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Early stage drug discovery would change dramatically if computational methods could accurately and quickly predict binding modes and affinities of compounds in advance of experiments. Simulation based approaches like classical molecular dynamics (MD) simulations have gained traction as a useful tool for early stage drug discovery, as MD provides a full atomistic and dynamic view of the biological system of interest. In principle, MD simulations can be a powerful tool for lead optimization as MD can provide knowledge of the ligand's binding mode, dynamics, and even binding affinity. In order to accurately compute binding affinities or predict ligand binding modes, simulations must run long enough to capture the relevant biological event or sufficiently sample all the physically relevant conformations. My research presents the development of new simulation approaches for accelerated sampling and demonstrates the use of non-equilibrium candidate Monte Carlo moves with MD simulations for predicting ligand binding modes to a pharmaceutically relevant target. |
| 일반주제명 | Computational chemistry. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
