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：
Thomas Edward Markland
金额：
$ 47.13万
依托单位：
STANFORD UNIVERSITY
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：
https://reporter.nih.gov/project-details/10589161
关键词：
Academia Acceleration Architecture Automobile Driving Biological Biological Process Biological Response Modifier Therapy Biology Chemical Models Chemicals Chemistry Code Communities Computer Vision Systems Custom Data Data Set Development Disease Ecosystem Ensure Event Free Energy Funding Future Goals Home Hybrids Industry Investigation Investments Laboratories Libraries Ligands Machine Learning Methods Modeling Modernization Molecular Molecular Conformation Performance Plug-in Productivity Proteins Pythons Research Research Personnel Running Sampling Science Specific qualifier value Speed Standardization Structure Study models Sustainable Development System Technology TensorFlow Training United States National Institutes of Health Update Work cluster computing data modeling deep learning deep neural network drug development enzyme mechanism flexibility insight interoperability machine learning framework machine learning model model development models and simulation molecular mechanics next generation novel therapeutics open source operation physical model predictive modeling protein data bank quantum repository simulation small molecule small molecule therapeutics software infrastructure tool

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]是生物分子的最广泛使用的开源GPU加速框架建模和仿真（> 1300个引用，> 270,000下载，> 1M部署实例）。它的python api制造它既广受欢迎，既是用于建模者）和库（用于开发人员）的应用程序，而其C/C ++/Fortran 绑定使主要的遗产模拟软件包能够使用OpenMM在现代上提供高性能硬件。 OpenMM已用于探测利用全球14b美元投资的生物学问题 PDB以多种量表的结构数据，从单个疾病蛋白的详细研究到超家族在工业和学术界进行建模研究和大规模的药物开发工作。我们最初是由斯坦福大学潘德实验室的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型，并扩展其能力以确保OpenMM能够为下十年的生物分子研究。用机器学习充分利用QM级别的革命（ML）电位，我们将添加针对由GPU加速内核增强的ML模型的插件支持，从而使具有QM级准确性的变革性科学。为了使新的ML模型的高生产率开发培训数据集尺寸接近1亿个分子，我们将开发一个Python框架以启用OpenMM 可以轻松地在现代ML框架中使用，例如Tensorflow和Pytorch。一起继续利用廉价GPU的优化，这些进步将为生物分子内的转换提供动力建模和仿真，就像深度学习改变了计算机视觉。