一侧可用的晶体结构数量越来越多,以及可用于计算机辅助药物设计任务的计算能力的提高,已经导致基于结构的药物设计工具被广泛用于药物开发管道。对接和分子动力学模拟,基于结构的方法的关键代表,提供有关配体与靶受体的潜在相互作用的详细信息。然而,同时,它们需要蛋白质的三维结构和相对大量的计算资源。如今,随着对接和分子动力学的应用越来越广泛,这些程序输出的数据量也在增长。因此,也有越来越多的方法,促进分析和解释基于结构的工具的结果。在这次审查中,我们将全面总结处理分子动力学模拟输出的方法。它将涵盖基于统计和机器学习的工具,以及各种形式的分子动力学输出的描述。
An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this
review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.