Computational Core
Computational Core
批准号:
10216261
负责人:
Tobias Kind
金额:
$82.43万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2024-06-30
关键词:
AddressAlgorithmic SoftwareBehaviorBiochemical ReactionBiologicalChargeChemicalsChromatographyCollaborationsCommunitiesComputer softwareDataDatabasesDescriptorDevelopmentDissociationElectronsEnsureEnzymesExclusionFingersFundingGasesGenerationsGoalsHybridsIonsIsomerismLibrariesLiteratureMachine LearningMass FragmentographyMechanicsMethodsModelingMolecular ConformationNucleotidesPhasePopulationProcessPropertyReactionReportingResearch PersonnelResourcesSamplingSoftware ToolsStructureTestingTimeUnited States National Institutes of HealthValidationWorkacylcarnitinebasedeep learningdensityelectronic structureexperimental studyheuristicshydrophilicityimprovedin silicoionizationknowledge baselearning strategymachine learning methodmetabolomicsmolecular dynamicsnovelpredictive modelingprogramsprotonationquantumquantum chemistrysmall moleculetheoriestoolvibrationvirtual
中文摘要
项目摘要-计算核心
西海岸代谢组学化合物鉴定中心(WCMC)的计算核心是
致力于NIH共同基金代谢组学倡议的总体目标,具体目标是大幅
改进小分子鉴定。计算核心领导者Kind博士和量子化学专家Kind教授。
坦蒂洛和王教授将与项目主任菲恩教授以及计算专家和程序员合作
关于以下具体目标。1)用于GC-MS的大型硅内质谱库的生成
以及LC-MS/MS应用于改善代谢组学中的化合物注释。这些硅内光谱库
将通过使用量子化学来创建,包括Born-Oppenheimer分子动力学和跃迁
状态反应模型。使用启发式和机器学习模型等经典方法
从计算预测的酶杂乱数据库中衍生出来的新化合物。2)
开发高精度的In-Silo碎裂系综,用于未知质量谱的快速排序。
这种方法在还没有参考光谱但化合物被覆盖的情况下是有利的
在现有的化学数据库中。这包括开发一条过滤管道,使用从
WCMC实验核心和包括额外的先验数据和文献参考。3)发展
GC-MS和LC-MS中多种化合物的高精度保留预测方法
用于化合物鉴定报告的正交滤波和提纯。软件工具和
数据库将与指导委员会和
管理核心。
英文摘要
Project Summary – Computational Core
The Computational Core at the West Coast Metabolomics Center for Compound Identification (WCMC) is
committed to the overall goals of the NIH Common Fund Metabolomics Initiative and specifically aims to greatly
improve small molecule identifications. Computational Core leader Dr. Kind and quantum chemistry experts Prof.
Tantillo and Prof. Wang will work with Program director Prof. Fiehn and computational experts and programmers
on the following specific aims. 1) The generation of large in-silico mass spectral libraries for use in both GC-MS
and LC-MS/MS applications to improve compound annotations in metabolomics. These in-silico spectral libraries
will be created by using quantum chemistry including Born-Oppenheimer molecular dynamics and transition
state reaction modelling. Inclusion of classical approaches such as heuristic and machine learning models using
novel compounds that are derived from computationally predicted enzyme promiscuity databases. 2) The
development of high accuracy in-silico fragmentation ensembles, for fast ranking of unknown mass spectra.
Such an approach is advantageous in case no reference spectrum is available yet, but the compound is covered
in existing chemical databases. This includes the development of a filtering pipeline with data obtained from the
WCMC Experimental Core and inclusion of additional priori data and literature references. 3) The development
of highly accurate retention prediction methods for a diverse and large set of compounds in GC-MS and LC-MS
to be used for orthogonal filtering and refinement of compound identification reports. The software tools and
databases will be independently validated and tested in close collaboration with the steering committee and the
Administrative Core.
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会议论文
Computational Core
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批准号:9767143
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项目类别:
-
资助金额:$45.66万
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财政年份:--
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负责人:Tobias Kind
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依托单位:
海外基金