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.

项目概要/摘要 OpenMM [http://openmm.org] 是最广泛使用的生物分子开源 GPU 加速框架建模和模拟（> 1300 次引用，> 270,000 次下载，> 100 万个部署实例）。它的Python API使得它作为应用程序（对于建模者）和库（对于开发人员）而广受欢迎，而它的 C/C++/Fortran 绑定使主要的传统模拟包能够使用 OpenMM 在现代平台上提供高性能硬件。 OpenMM 已被用于探索生物学问题，这些问题利用了 $14B 全球投资来自 PDB 的多尺度结构数据，从单一疾病蛋白的详细研究到超家族范围的研究工业界和学术界的建模研究和大规模药物开发工作。最初由斯坦福大学 Pande 实验室在 NIH 资助下开发，我们的目标是完全向社区过渡治理和可持续发展模型并扩展其功能，以确保 OpenMM 能够为生物分子研究的下一个十年。通过机器学习充分利用 QM 级精度的革命 (ML) 潜力，我们将为通过 GPU 加速内核增强的 ML 模型添加插件支持，从而实现具有 QM 级准确性的变革性科学。为了实现新机器学习模型的高生产率开发训练数据集大小接近 1 亿个分子，我们将开发一个 Python 框架来启用 OpenMM 可以在 TensorFlow 和 PyTorch 等现代机器学习框架中轻松使用。连同继续优化以利用廉价的 GPU，这些进步将推动生物分子领域的变革建模和模拟，就像深度学习改变了计算机视觉一样。