OpenMM: Scalable biomolecular modeling, simulation, and machine learning

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(&gt；1300次引用，&gt；27万次下载，&gt；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。

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.