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Computational Methods in Drug Discovery: From Molecular Modeling to Library Design
Computational Methods in Drug Discovery: From Molecular Modeling to Library Design
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151133
- ISBN
- 9798383596883
- DDC
- 540
- 저자명
- Zhang, Chris.
- 서명/저자
- Computational Methods in Drug Discovery: From Molecular Modeling to Library Design
- 발행사항
- [Sl] : University of California, Irvine, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 170 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Mobley, David.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Irvine, 2024.
- 초록/해제
- 요약Advancements in computational methods have significantly impacted the field of drug discovery by enabling the exploration of complex molecular interactions and the design of diverse chemical libraries. This dissertation presents a study into various computational approaches aimed at enhancing the efficiency and efficacy of early stage drug discovery. In Chapter 2, we explore how machine learning methods can be used to more efficiently select compounds within large chemical databases. We demonstrate how active learning approaches identify promising drug candidates with reduced computational cost and how machine learning (ML) models can be used to filter large chemical databases. We then shift our focus to characterizing discrete binding conformations of T4 L99A using Markov state models (MSMs) in Chapter 3. Using MSMs, we characterize the dynamic behavior of protein-ligand interactions and provide insights into the binding mechanisms crucial to rational drug design that need to be addressed in future studies. Chapters 4 and 5 delve into strategies for building block selection in DNA-encoded library (DEL) design. Leveraging building block-centric approaches, we provide guidelines to construct libraries under specific design constraints and develop predictive models to inform prior additional computational and experimental follow-up. Collectively, we discuss a diverse set of computational techniques which we hope will lead to more efficient and effective strategies for drug design and library construction in the future.
- 일반주제명
- Chemistry
- 일반주제명
- Computational chemistry
- 일반주제명
- Pharmaceutical sciences
- 키워드
- Drug discovery
- 기타저자
- University of California, Irvine Chemistry
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798383596883
■035 ▼a(MiAaPQ)AAI31147879
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a540
■1001 ▼aZhang, Chris.
■24510▼aComputational Methods in Drug Discovery: From Molecular Modeling to Library Design
■260 ▼a[Sl]▼bUniversity of California, Irvine▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a170 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Mobley, David.
■5021 ▼aThesis (Ph.D.)--University of California, Irvine, 2024.
■520 ▼aAdvancements in computational methods have significantly impacted the field of drug discovery by enabling the exploration of complex molecular interactions and the design of diverse chemical libraries. This dissertation presents a study into various computational approaches aimed at enhancing the efficiency and efficacy of early stage drug discovery. In Chapter 2, we explore how machine learning methods can be used to more efficiently select compounds within large chemical databases. We demonstrate how active learning approaches identify promising drug candidates with reduced computational cost and how machine learning (ML) models can be used to filter large chemical databases. We then shift our focus to characterizing discrete binding conformations of T4 L99A using Markov state models (MSMs) in Chapter 3. Using MSMs, we characterize the dynamic behavior of protein-ligand interactions and provide insights into the binding mechanisms crucial to rational drug design that need to be addressed in future studies. Chapters 4 and 5 delve into strategies for building block selection in DNA-encoded library (DEL) design. Leveraging building block-centric approaches, we provide guidelines to construct libraries under specific design constraints and develop predictive models to inform prior additional computational and experimental follow-up. Collectively, we discuss a diverse set of computational techniques which we hope will lead to more efficient and effective strategies for drug design and library construction in the future.
■590 ▼aSchool code: 0030.
■650 4▼aChemistry
■650 4▼aComputational chemistry
■650 4▼aPharmaceutical sciences
■653 ▼aMarkov state models
■653 ▼aMachine learning models
■653 ▼aDrug discovery
■653 ▼aComputational techniques
■690 ▼a0485
■690 ▼a0219
■690 ▼a0800
■690 ▼a0572
■71020▼aUniversity of California, Irvine▼bChemistry.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0030
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17160900▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
