OpenMM: Scalable biomolecular modeling, simulation, and machine learning
OpenMM:可扩展的生物分子建模、模拟和机器学习
基本信息
- 批准号:10589161
- 负责人:
- 金额:$ 47.13万
- 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
- 起止时间:2021-07-01 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:AcademiaAccelerationArchitectureAutomobile DrivingBiologicalBiological ProcessBiological Response Modifier TherapyBiologyChemical ModelsChemicalsChemistryCodeCommunitiesComputer Vision SystemsCustomDataData SetDevelopmentDiseaseEcosystemEnsureEventFree EnergyFundingFutureGoalsHomeHybridsIndustryInvestigationInvestmentsLaboratoriesLibrariesLigandsMachine LearningMethodsModelingModernizationMolecularMolecular ConformationPerformancePlug-inProductivityProteinsPythonsResearchResearch PersonnelRunningSamplingScienceSpecific qualifier valueSpeedStandardizationStructureStudy modelsSustainable DevelopmentSystemTechnologyTensorFlowTrainingUnited States National Institutes of HealthUpdateWorkcluster computingdata modelingdeep learningdeep neural networkdrug developmentenzyme mechanismflexibilityinsightinteroperabilitymachine learning frameworkmachine learning modelmodel developmentmodels and simulationmolecular mechanicsnext generationnovel therapeuticsopen sourceoperationphysical modelpredictive modelingprotein data bankquantumrepositorysimulationsmall moleculesmall molecule therapeuticssoftware infrastructuretool
项目摘要
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.
项目摘要/摘要
OpenMM[http://openmm.org]是使用最广泛的开源图形处理器加速生物分子框架
建模和模拟(>;1300次引用,>;27万次下载,>;100万个已部署实例)。它的Python API使
它作为应用程序(对于建模人员)和库(对于开发人员)都很受欢迎,而它的C/C++/Fortran
绑定使主要的传统模拟包能够使用OpenMM在现代
硬件。OpenMM已被用于探索生物问题,这些问题利用了140亿美元的全球投资
来自PDB的多个尺度的结构数据,从对单个疾病蛋白的详细研究到超家族范围
工业界和学术界的建模研究和大规模药物开发工作。
最初是由斯坦福大学潘德实验室由NIH资助开发的,我们的目标是完全过渡到一个社区
治理和可持续发展模式,并扩展其能力,以确保OpenMM能够为
下一个十年的生物分子研究。用机器学习充分利用QM级精度的革命
(ML)潜力,我们将添加对由GPU加速的内核增强的ML模型的插件支持,从而实现
具有QM级别精度的变革性科学。要实现新ML模型的高生产率开发,请使用
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
便于在TensorFlow和PyTorch等现代ML框架中使用。连同继续
优化以开发廉价的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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Thomas Edward Markland其他文献
Thomas Edward Markland的其他文献
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{{ truncateString('Thomas Edward Markland', 18)}}的其他基金
OpenMM: Scalable biomolecular modeling, simulation, and machine learning
OpenMM:可扩展的生物分子建模、模拟和机器学习
- 批准号:
10441130 - 财政年份:2021
- 资助金额:
$ 47.13万 - 项目类别:
OpenMM: Scalable biomolecular modeling, simulation, and machine learning
OpenMM:可扩展的生物分子建模、模拟和机器学习
- 批准号:
10587054 - 财政年份:2021
- 资助金额:
$ 47.13万 - 项目类别:
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