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：
10587054
负责人：
Thomas Edward Markland
金额：
$ 12.38万
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/10587054
关键词：
Academia Architecture Automobile Driving Binding 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 Learning Libraries Ligands Machine Learning Methods Modeling Modernization Molecular Molecular Conformation Performance Plug-in Productivity Proteins Pythons Research Research Personnel Rest Running Sampling Science Speed Standardization Structure Study models Sustainable Development System Technology TensorFlow Training United States National Institutes of Health Update Work cluster computing 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 portability 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]是最广泛使用的开源GPU-ACCELERATED框架生物分子建模和仿真（> 1300个引用，> 270,000下载，> 1M部署实例）。它的Python API使其既广受欢迎又是一个应用程序（用于建模者）和库（对于开发人员），而其c/c ++/fortran绑定使主要的遗产仿真软件包可以使用 OpenMM可在现代硬件上提供高性能。 OpenMM已用于探测利用PDB的$ 14B全球投资的生物学问题多个量表，从单疾病蛋白的详细研究到超家族的建模研究以及行业和学术界的大规模药物开发工作。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社区治理和可持续发展模型，并扩展其能力以确保 OpenMM可以在接下来的十年生物分子研究中供电。充分利用QM的革命机器学习（ML）电位的水平准确性，我们将为ML型号添加插入式支持由GPU加速核增强，以QM级的精度为变革性科学。到启用具有培训数据集尺寸的新ML模型的高生产率开发，接近100 百万分子，我们将开发一个Python框架，使OpenMM可以轻松使用现代ML框架，例如Tensorflow和Pytorch。以及继续优化利用廉价的GPU，这些进步将为生物分子建模内的转换提供动力和模拟，深度学习已经改变了计算机视觉。