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Developing Artificial Intelligence Tools for Biologists
Developing Artificial Intelligence Tools for Biologists
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151352
- ISBN
- 9798382813752
- DDC
- 574
- 저자명
- Shub, Laura.
- 서명/저자
- Developing Artificial Intelligence Tools for Biologists
- 발행사항
- [Sl] : University of California, San Francisco, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 156 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Keiser, Michael.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Francisco, 2024.
- 초록/해제
- 요약With the growth of biological and chemical datasets and the development of novel computational techniques, applications of artificial intelligence (AI) and machine learning (ML) methods that leverage these datasets to assist experimentalists become more critical than ever. This dissertation presents an overview of commonly used AL/ML tools for molecular biology and introduces two novel tools, as well as details their specific use cases. In Chapter 1, I provide a review of traditional techniques and their machine learning counterparts for ligand- and structure-based drug discovery and protein structure elucidation and design. In Chapter 2, I introduce Metric Ion Classification (MIC), a method for determining the identity of experimentally identified waters and ions in biomolecular structures. MIC builds upon recent advancements in protein-ligand interface representations and metric learning techniques to introduce a novel classification scheme with extensive validation on a variety of experimental structures. In Chapter 3, we present Autoparty, a tool for AI-assisted human-in-the-loop molecule annotation designed to facilitate the manual assessment of virtual screening results. Autoparty uses the principles of active learning to direct chemists toward useful compounds and limit the amount of labor required when evaluating compounds. These applications do not attempt to replace existing techniques; rather, they act in service of scientists to accelerate both structure determination and drug discovery pipelines. This work broadly highlights the utility of these tools and others like them and encourages their adoption alongside classical approaches.
- 일반주제명
- Bioinformatics
- 일반주제명
- Computational chemistry
- 일반주제명
- Pharmaceutical sciences
- 키워드
- Drug discovery
- 키워드
- Machine learning
- 기타저자
- University of California, San Francisco Biological and Medical Informatics
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■007cr#unu||||||||
■020 ▼a9798382813752
■035 ▼a(MiAaPQ)AAI31243428
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aShub, Laura.▼0(orcid)0000-0003-0211-0396
■24510▼aDeveloping Artificial Intelligence Tools for Biologists
■260 ▼a[Sl]▼bUniversity of California, San Francisco▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a156 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Keiser, Michael.
■5021 ▼aThesis (Ph.D.)--University of California, San Francisco, 2024.
■520 ▼aWith the growth of biological and chemical datasets and the development of novel computational techniques, applications of artificial intelligence (AI) and machine learning (ML) methods that leverage these datasets to assist experimentalists become more critical than ever. This dissertation presents an overview of commonly used AL/ML tools for molecular biology and introduces two novel tools, as well as details their specific use cases. In Chapter 1, I provide a review of traditional techniques and their machine learning counterparts for ligand- and structure-based drug discovery and protein structure elucidation and design. In Chapter 2, I introduce Metric Ion Classification (MIC), a method for determining the identity of experimentally identified waters and ions in biomolecular structures. MIC builds upon recent advancements in protein-ligand interface representations and metric learning techniques to introduce a novel classification scheme with extensive validation on a variety of experimental structures. In Chapter 3, we present Autoparty, a tool for AI-assisted human-in-the-loop molecule annotation designed to facilitate the manual assessment of virtual screening results. Autoparty uses the principles of active learning to direct chemists toward useful compounds and limit the amount of labor required when evaluating compounds. These applications do not attempt to replace existing techniques; rather, they act in service of scientists to accelerate both structure determination and drug discovery pipelines. This work broadly highlights the utility of these tools and others like them and encourages their adoption alongside classical approaches.
■590 ▼aSchool code: 0034.
■650 4▼aBioinformatics
■650 4▼aComputational chemistry
■650 4▼aPharmaceutical sciences
■653 ▼aDrug discovery
■653 ▼aMachine learning
■653 ▼aProtein structure elucidation
■653 ▼aMetric learning techniques
■690 ▼a0715
■690 ▼a0219
■690 ▼a0800
■690 ▼a0572
■71020▼aUniversity of California, San Francisco▼bBiological and Medical Informatics.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0034
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161411▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
