Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

中文翻译：

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通过扩展的连续相似性指数进行分子动力学模拟和多样性选择

分子动力学 (MD) 是分子建模和计算设计的核心方法，用于研究分子系统的动力学和时间演化。MD 模拟特别受益于计算能力的快速增长，这是过去几十年计算化学研究的特点，是第一种成功迁移到 GPU 基础设施的方法。虽然新一代 MD 软件能够以不断扩大的规模提供模拟，但在开发能够跟上正在生成的快速增长的数据量的后处理方法上投入的精力相对较少。在这里，我们基于最近引入的扩展相似性指数框架，介绍了从大型 MD 轨迹中采样帧的新想法。我们的方法提出了一种新的、线性缩放替代应用聚类算法的传统方法，该聚类算法通常缩放为帧数的二次函数。在展示其在具有不同系统规模和模拟长度的案例研究中的应用时，与传统的聚类算法相比，我们已经记录到了高达 2 个数量级的加速。所选框架的构象多样性也明显更高，这对于某些应用来说是另一个优势，例如选择用于配体对接的结构集合。该方法可在 https://github.com/ramirandaq/MultipleComparisons 开源。在展示其在具有不同系统规模和模拟长度的案例研究中的应用时，与传统的聚类算法相比，我们已经记录到了高达 2 个数量级的加速。所选框架的构象多样性也明显更高，这对于某些应用来说是另一个优势，例如选择用于配体对接的结构集合。该方法可在 https://github.com/ramirandaq/MultipleComparisons 开源。在展示其在具有不同系统规模和模拟长度的案例研究中的应用时，与传统的聚类算法相比，我们已经记录到了高达 2 个数量级的加速。所选框架的构象多样性也明显更高，这对于某些应用来说是另一个优势，例如选择用于配体对接的结构集合。该方法可在 https://github.com/ramirandaq/MultipleComparisons 开源。