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

下一代可扩展的仿真环境，提供经济、准确、高效的免费 能量模拟 PI：Tai-Sung Lee，罗格斯大学，皮斯卡特维，NJ 08854-8087 USA。 多尺度分子模拟对科学研究至关重要，影响蛋白质和核酸 工程、生物材料设计和药物发现。AMBER，一个具有广泛学术价值的仿真软件包 自20世纪80年代以来，该公司一直致力于为全球30，000多名用户提供支持。琥珀的持久性 流行归功于其pmemd.cuda模拟引擎，最佳实施负担得起的图形处理 单元（GPU）平台。最近，用现代自由能方法扩展了实施， 新颖的采样算法，以支持一系列的科学应用，其中主要是药物发现， 蛋白质-配体结合亲和力的预测。新一代的集成方法、工作流程和 物理模型正在出现，由包括NAMD在内的一系列模拟软件支持， CHARMM、Gromacs、OpenMM和其他程序。随着这些科学进步， 在该领域的进展是缺乏一个软件包与无缝结合的高性能 自由能估算器和新兴模型。为了克服这一障碍， 重点技术资助机会，要求创新，重点技术开发， 工作原型的关键研究工具，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，新算法的可访问性， 单GPU性能，在同步和异步集成方法中具有强大的扩展能力， 与不同类型的力场和物理模型的互操作性。我们建议采取以下措施：1. 环境：基于Python的执行环境，用于集成各种模块，包括 MD引擎、自由能量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，并为科学和算法进步的未来增长做好准备。2.优化：新 由OOP CPU层和高性能计算组成的MD和自由能模块 (HPC)内核，由运行时管理机制共同优化。C++的紧密耦合 层和HPC内核扩展构成了一个系统，可优化主机和 HPC设备。我们的目标是一组高效、健壮和可扩展的核心仿真模块， 各个模块级别及其共同执行。3.应用：自动扣款套件 炼金术的图表。这个工具构建在新的Python环境中，将集合新的MD引擎 和集成功能，以展示其可扩展性和多功能性。它也将作为一个例子， 未来的开发人员如何重组积木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.