OpenMM: Scalable biomolecular modeling, simulation, and machine learning
OpenMM: Scalable biomolecular modeling, simulation, and machine learning
批准号:
10441130
负责人:
Thomas Edward Markland
金额:
$47.19万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-07-01 至 2025-03-31
关键词:
AcademiaArchitectureAutomobile DrivingBindingBiologicalBiological ProcessBiological Response Modifier TherapyBiologyChemical ModelsChemicalsChemistryCodeCommunitiesComputer Vision SystemsCustomDataData SetDevelopmentDiseaseEcosystemEnsureEventFree EnergyFundingFutureGoalsHomeHybridsIndustryInvestigationInvestmentsLaboratoriesLearningLibrariesLigandsMachine LearningMethodsModelingModernizationMolecularMolecular ConformationPerformancePlug-inProductivityProteinsPythonsResearchResearch PersonnelRunningSamplingScienceSpeedStandardizationStructureStudy modelsSustainable DevelopmentSystemTechnologyTensorFlowTrainingUnited States National Institutes of HealthUpdateWorkcluster computingdeep learningdeep neural networkdrug developmentenzyme mechanismflexibilityinsightinteroperabilitymachine learning frameworkmachine learning modelmodel developmentmodels and simulationmolecular mechanicsnext generationnovel therapeuticsopen sourceoperationphysical modelpredictive modelingprotein data bankquantumrepositorysimulationsmall moleculesmall molecule therapeuticssoftware infrastructuretool
中文摘要
项目概要/摘要
OpenMM [http://openmm.org]是最广泛使用的开源GPU加速框架,用于生物分子
建模和仿真(>1300次引用,> 270,000次下载,> 1 M部署实例)。它的Python API使
它作为一个应用程序(对于建模者)和一个库(对于开发者)广泛流行,而它的C/C++/Fortran
绑定使主要的遗留仿真包能够使用OpenMM,
硬件. OpenMM已被用于探索生物学问题,这些问题利用了全球140亿美元的投资,
PDB在多个尺度上的结构数据,从单个疾病蛋白的详细研究到整个超家族
在工业界和学术界开展建模研究和大规模药物开发工作。
最初由斯坦福大学的Pande实验室在NIH的资助下开发,我们的目标是完全过渡到一个社区
管理和可持续发展模式,并扩展其能力,以确保OpenMM能够为
生物分子研究的未来十年通过机器学习充分利用QM级精度的革命
(ML)潜力,我们将添加插件支持由GPU加速内核增强的ML模型,
具有QM级精度的变革性科学。为了实现新ML模型的高生产力开发,
训练数据集大小接近1亿个分子,我们将开发一个Python框架来支持OpenMM
可以在现代ML框架(如TensorFlow和PyTorch)中轻松使用。加上持续
优化利用廉价的GPU,这些进步将推动生物分子内的转变
建模和仿真,就像深度学习改变了计算机视觉一样。
英文摘要
PROJECT SUMMARY / ABSTRACT
OpenMM [http://openmm.org] is the most widely-used open source GPU-accelerated framework for biomolecular
modeling and simulation (>1300 citations, >270,000 downloads, >1M deployed instances). Its Python API makes
it widely popular as both an application (for modelers) and a library (for developers), while its C/C++/Fortran
bindings enable major legacy simulation packages to use OpenMM to provide high performance on modern
hardware. OpenMM has been used for probing biological questions that leverage the $14B global investment in
structural data from the PDB at multiple scales, from detailed studies of single disease proteins to superfamily-wide
modeling studies and large-scale drug development efforts in industry and academia.
Originally developed with NIH funding by the Pande lab at Stanford, we aim to fully transition toward a community
governance and sustainable development model and extend its capabilities to ensure OpenMM can power the
next decade of biomolecular research. To fully exploit the revolution in QM-level accuracy with machine-learning
(ML) potentials, we will add plug-in support for ML models augmented by GPU-accelerated kernels, enabling
transformative science with QM-level accuracy. To enable high-productivity development of new ML models with
training dataset sizes approaching 100 million molecules, we will develop a Python framework to enable OpenMM
to be easily used within modern ML frameworks such as TensorFlow and PyTorch. Together with continued
optimizations to exploit inexpensive GPUs, these advances will power a transformation within biomolecular
modeling and simulation, much as deep learning has transformed computer vision.
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会议论文
OpenMM: Scalable biomolecular modeling, simulation, and machine learning
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批准号:10587054
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项目类别:
-
资助金额:$12.38万
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财政年份:2021
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负责人:Thomas Edward Markland
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依托单位:
OpenMM: Scalable biomolecular modeling, simulation, and machine learning
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批准号:10589161
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项目类别:
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资助金额:$47.13万
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财政年份:2021
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负责人:Thomas Edward Markland
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依托单位:
海外基金