A survey of algorithms for transforming molecular dynamics data into metadata for in situ analytics based on machine learning methods

This paper presents the survey of three algorithms to transform atomic-level molecular snapshots from molecular dynamics (MD) simulations into metadata representations that are suitable for in situ analytics based on machine learning methods. MD simulations studying the classical time evolution of a molecular system at atomic resolution are widely recognized in the fields of chemistry, material sciences, molecular biology and drug design; these simulations are one of the most common simulations on supercomputers. Next-generation supercomputers will have a dramatically higher performance than current systems, generating more data that needs to be analysed (e.g. in terms of number and length of MD trajectories). In the future, the coordination of data generation and analysis can no longer rely on manual, centralized analysis traditionally performed after the simulation is completed or on current data representations that have been defined for traditional visualization tools. Powerful data preparation phases (i.e. phases in which original row data is transformed to concise and still meaningful representations) will need to proceed data analysis phases. Here, we discuss three algorithms for transforming traditionally used molecular representations into concise and meaningful metadata representations. The transformations can be performed locally. The new metadata can be fed into machine learning methods for runtime in situ analysis of larger MD trajectories supported by high-performance computing. In this paper, we provide an overview of the three algorithms and their use for three different applications: protein-ligand docking in drug design; protein folding simulations; and protein engineering based on analytics of protein functions depending on proteins' three-dimensional structures. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'.

本文综述了三种算法，这些算法可将分子动力学（MD）模拟中的原子级分子快照转换为适用于基于机器学习方法的原位分析的元数据表示形式。在原子分辨率下研究分子系统经典时间演化的MD模拟在化学、材料科学、分子生物学和药物设计等领域得到广泛认可；这些模拟是超级计算机上最常见的模拟之一。下一代超级计算机的性能将比当前系统大幅提高，会产生更多需要分析的数据（例如，就MD轨迹的数量和长度而言）。未来，数据生成和分析的协调不能再依赖于模拟完成后传统上进行的手动、集中式分析，也不能依赖于为传统可视化工具定义的当前数据表示形式。强大的数据准备阶段（即将原始行数据转换为简洁且仍有意义的表示形式的阶段）将需要先于数据分析阶段进行。在此，我们讨论了三种用于将传统使用的分子表示形式转换为简洁且有意义的元数据表示形式的算法。这些转换可在本地进行。新的元数据可输入机器学习方法，以便在高性能计算支持下对更大的MD轨迹进行运行时原位分析。在本文中，我们概述了这三种算法及其在三个不同应用中的使用：药物设计中的蛋白质 - 配体对接；蛋白质折叠模拟；以及基于蛋白质三维结构对蛋白质功能进行分析的蛋白质工程。本文是“高性能计算科学的数值算法”讨论会议议题的一部分。