서브메뉴
검색
Artificial Intelligence for Accelerating and Understanding Molecular Simulations
Artificial Intelligence for Accelerating and Understanding Molecular Simulations
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104807
- ISBN
- 9798293836499
- DDC
- 574.191
- 저자명
- Mehdi, Shams.
- 서명/저자
- Artificial Intelligence for Accelerating and Understanding Molecular Simulations
- 발행사항
- [Sl] : University of Maryland, College Park, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 187 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Tiwary, Pratyush.
- 학위논문주기
- Thesis (Ph.D.)--University of Maryland, College Park, 2025.
- 초록/해제
- 요약Computational techniques such as molecular dynamics (MD) simulations offer detailed spatiotemporal insights into biomolecular systems, playing a critical role in uncovering mechanisms and informing therapeutic design. However, a major limitation of MD is its high computational cost, which makes it challenging to study many biophysically relevant processes.In this dissertation, I address this challenge by integrating artificial intelligence (AI) with statistical physics to develop enhanced sampling methods that significantly accelerate MD simulations. These physics-driven, representation learning approaches enable efficient implementation of molecular dynamics in systems that would otherwise be computationally intractable. Moving beyond traditional model systems, I demonstrate the practical utility of these methods in drug discovery, particularly in characterizing the interactions between small molecules and RNA, an emerging class of therapeutic targets.Furthermore, such AI-driven approaches typically operate in data-sparse regimes and it is essential to establish the robustness of the trained models before deploying them. For this purpose, I design an algorithm to validate general-purpose AI models, particularly in the context of MD.Overall, this dissertation demonstrates how the principled integration of AI with physics-based computational methods enables more efficient and insightful molecular simulations.
- 일반주제명
- Biophysics
- 일반주제명
- Chemistry
- 일반주제명
- Statistical physics
- 키워드
- Explainable AI
- 키워드
- Machine learning
- 키워드
- RNA therapeutics
- 기타저자
- University of Maryland, College Park Biophysics (BIPH)
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017358897
■00520260202104807
■006m o d
■007cr#unu||||||||
■020 ▼a9798293836499
■035 ▼a(MiAaPQ)AAI32165898
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574.191
■1001 ▼aMehdi, Shams.▼0(orcid)0000-0002-4078-7501
■24510▼aArtificial Intelligence for Accelerating and Understanding Molecular Simulations
■260 ▼a[Sl]▼bUniversity of Maryland, College Park▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a187 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Tiwary, Pratyush.
■5021 ▼aThesis (Ph.D.)--University of Maryland, College Park, 2025.
■520 ▼aComputational techniques such as molecular dynamics (MD) simulations offer detailed spatiotemporal insights into biomolecular systems, playing a critical role in uncovering mechanisms and informing therapeutic design. However, a major limitation of MD is its high computational cost, which makes it challenging to study many biophysically relevant processes.In this dissertation, I address this challenge by integrating artificial intelligence (AI) with statistical physics to develop enhanced sampling methods that significantly accelerate MD simulations. These physics-driven, representation learning approaches enable efficient implementation of molecular dynamics in systems that would otherwise be computationally intractable. Moving beyond traditional model systems, I demonstrate the practical utility of these methods in drug discovery, particularly in characterizing the interactions between small molecules and RNA, an emerging class of therapeutic targets.Furthermore, such AI-driven approaches typically operate in data-sparse regimes and it is essential to establish the robustness of the trained models before deploying them. For this purpose, I design an algorithm to validate general-purpose AI models, particularly in the context of MD.Overall, this dissertation demonstrates how the principled integration of AI with physics-based computational methods enables more efficient and insightful molecular simulations.
■590 ▼aSchool code: 0117.
■650 4▼aBiophysics
■650 4▼aChemistry
■650 4▼aStatistical physics
■653 ▼aEnhanced sampling
■653 ▼aExplainable AI
■653 ▼aMachine learning
■653 ▼aRNA therapeutics
■653 ▼aMolecular dynamics
■690 ▼a0786
■690 ▼a0800
■690 ▼a0485
■690 ▼a0217
■71020▼aUniversity of Maryland, College Park▼bBiophysics (BIPH).
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0117
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358897▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
