Molecular dynamics (MD) has become a powerful tool for studying biophysical systems, due to increasing computational power and availability of software. Although MD has made many contributions to better understanding these complex biophysical systems, there remain methodological difficulties to be surmounted. First, how to make the deluge of data generated in running even a microsecond long MD simulation human comprehensible. Second, how to efficiently sample the underlying free energy surface and kinetics. In this short perspective, we summarize machine learning based ideas that are solving both of these limitations, with a focus on their key theoretical underpinnings and remaining challenges.

中文翻译：

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用于分析和增强分子动力学模拟的机器学习方法。

由于计算能力和软件可用性的提高，分子动力学（MD）已成为研究生物物理系统的强大工具。尽管医学博士为更好地理解这些复杂的生物物理系统做出了许多贡献，但是仍然存在方法上的困难。首先，如何使即使在微秒级的MD模拟运行中产生的大量数据也易于理解。其次，如何有效地采样潜在的自由能表面和动力学。在这个简短的观点中，我们总结了基于机器学习的思想，这些思想解决了这两个局限性，并将重点放在了其关键的理论基础和尚待解决的挑战上。