论文标题
管理数据并行计算工作流的经验，用于高通量片段分子轨道（FMO）计算
Experiences with managing data parallel computational workflows for High-throughput Fragment Molecular Orbital (FMO) Calculations
论文作者
论文摘要
片段分子轨道（FMO）计算提供了一个框架来加快量子机械计算的速度，因此可用于探索大而复杂的生物分子系统中的结构 - 能源关系。这些计算仍然很繁重，尤其是应用于大量分子时。因此，需要为这些计算的机制和用户界面提供机制和用户界面的网络基础结构。由于需要快速识别可能与可能与SARS-COV-2有关的靶标的药物（导致Covid-19的病毒）结合的动机，我们开发了一个使用Apache Airavata中间件的静态参数扫描框架，以适用于SARS-COV-2 M-PRO之间形成的复合物（SARS-COV-2 M-Pro之间）在这里，我们描述了管理高通量FMO计算的框架的实现。该方法是一般的，因此应该在生物分子系统的大规模FMO计算中找到效用。
Fragment Molecular Orbital (FMO) calculations provide a framework to speed up quantum mechanical calculations and so can be used to explore structure-energy relationships in large and complex biomolecular systems. These calculations are still onerous, especially when applied to large sets of molecules. Therefore, cyberinfrastructure that provides mechanisms and user interfaces that manage job submissions, failed job resubmissions, data retrieval, and data storage for these calculations are needed. Motivated by the need to rapidly identify drugs that are likely to bind to targets implicated in SARS-CoV-2, the virus that causes COVID-19, we developed a static parameter sweeping framework with Apache Airavata middleware to apply to complexes formed between SARS-CoV-2 M-pro (the main protease in SARS-CoV-2) and 2820 small-molecules in a drug-repurposing library. Here we describe the implementation of our framework for managing the executions of the high-throughput FMO calculations. The approach is general and so should find utility in large-scale FMO calculations on biomolecular systems.
