A next-generation extendable simulation environment for affordable, accurate, and efficient free energy simulations

下一代可扩展的模拟环境，提供经济、准确、高效的免费 能源模拟 派：李泰成，罗格斯大学，美国新泽西州皮斯卡特韦，邮编08854-8087。 多尺度分子模拟对影响蛋白质和核酸的科学研究至关重要 工程、生物材料设计和药物研发。Amber，一个具有广泛学术意义的模拟程序包 自20世纪80年代以来一直具有行业吸引力，今天支持全球超过30,000名用户。琥珀经久不衰 广受欢迎归功于其pmemd.cuda模拟引擎，以最佳方式实现了负担得起的图形处理 单元(GPU)平台。最近，用现代自由能方法和 支持一系列科学应用的新型采样算法，主要是通过 蛋白质-配体结合亲和力的预测。新一代集成的方法、工作流和 在包括NAMD在内的一系列模拟软件的支持下，物理模型正在涌现， CHARMM、Gromacs、OpenMM等程序。随着这些科学进步，一个关键的障碍是 该领域的进展是缺乏一个无缝结合高性能的软件包 MD、自由能估计器和新兴模型。为了跨越这一障碍，在应对美国国立卫生研究院 有重点的技术资助机会，需要创新、有重点的技术开发 在关键研究工具的工作原型基础上，我们建议开发下一代执行环境。 拟议的工作将提供快速原型制作、新算法的可访问性、进一步改进 单GPU性能，同步和异步集成方法的强大伸缩性，以及 与不同类型的力场和物理模型的互操作性。我们建议做以下工作：1. 环境：基于Python的执行环境，用于集成各种模块，包括 MD引擎、自由能模块和用户工作流控制。这是链接运行时的基本结构 对象将为连接分子动力学、模型构建和分析奠定基础 组件，并为未来科学和算法进步的增长做好准备。2.优化：新增 MD和自由能模块，由面向对象的CPU层和高性能计算组成 (HPC)内核，由运行时管理机制共同优化。C++的紧耦合 层和HPC内核扩展构成了一个优化主机和 HPC设备。目标是一组高效、健壮、可扩展的核心仿真模块，在这两个方面都进行了优化 单独的模块级别和它们的集体执行。3.应用：自动扣款套装 炼金术的图表。该设施构建在新的Python环境中，将组装新的MD引擎 和集成功能，以展示其可扩展性和多功能性。它还将作为一个榜样，以 未来的开发人员如何重新组合构建块来推进他们自己的科学。

A next-generation extendable simulation environment for affordable, accurate, and efficient free energy simulations PI: Tai-Sung Lee, Rutgers University, Piscataway, NJ 08854-8087 USA. Multi-scale molecular simulations are vital to scientific research, impacting protein and nucleic acid engineering, biomaterials design, and drug discovery. AMBER, a simulation package with broad academic and industrial appeal since the 1980s, supports over 30,000 users across the world today. AMBER’s enduring popularity owes to its pmemd.cuda simulation engine, optimally implemented affordable Graphics Processing Unit (GPU) platforms. Recently, the implementation was extended with modern free energy methods and novel sampling algorithms to support a range of scientific applications, chief among them drug discovery by prediction of protein-ligand binding affinities. A new generation of integrated methods, workflows, and physical models are emerging, supported by a constellation of simulation software that includes NAMD, CHARMM, Gromacs, OpenMM, and other programs. With these scientific advances a critical barrier to progress in the field is the lack of a software package with seamless incorporation of high-performance MD, free energy estimators, and emerging models. To surmount this barrier and in responding to NIH Focused Technology Funding Opportunity which calls for innovative, focused technology development of a working prototype of critical research tools, we propose to develop a next-generation executing environment. The proposed work will offer fast prototyping, accessibility to new algorithms, further improvements in single-GPU performance, strong scaling in synchronous and asynchronous ensemble methods, and interoperability with different types of force fields and physical models. We propose to do the following: 1. Environment: A Python-based executing environment for integrating various modules, including the MD engine, the free energy module, and user-workflow control. This essential fabric for linking runtime objects will create a foundation for connecting the molecular dynamics, model building, and analysis components, and is ready for future growth of scientific and algorithmic advances. 2. Optimization: New MD and free energy modules consisting of OOP CPU layers and high performance computing (HPC) kernels, collectively optimized by runtime managing mechanisms. Tight coupling of the C++ layer and the HPC kernel extensions constitutes a system that optimizes performance on both the host and HPC device. The goal is a set of efficient, robust, and extensible core simulation modules optimized at both individual module level and their collectively execution. 3. Application: A suite for automatic deduction of alchemical graphs. This facility, built in the new Python environment, will muster the new MD engine and integrated capabilities to showcase their scaling and versatility. It will also serve as an example to future developers of how to recombine the building blocks for advancing their own science.