In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods.

近年来，通过快速、准确、精确和可重复的自由能计算来计算与蛋白质结合的化合物的结合亲和力已经成为可能。这对于药物发现和个性化医疗至关重要。该方法基于分子动力学 (MD) 模拟，并利用相关蛋白质和化合物的序列和结构信息。自由能量由许多 MD 副本的整体平均值决定，每个 MD 副本都需要数百个核心和/或 GPU 加速器，这些加速器现在可以在商用云计算平台上使用；初始模型构建和后续数据分析阶段也有要求。为了使该过程自动化，我们开发了一种称为结合亲和力计算器的工作流程。在本文中，我们重点关注我们开发的软件基础设施和接口，用于自动化整个工作流程并在商品云平台上执行它，以便可靠地预测它们在与应用领域相关的时间尺度上的结合亲和力，并说明其应用于两种自由能方法。