The accurate prediction of thermodynamic properties is crucial in various fields such as drug discovery and materials design. This task relies on sampling from the underlying Boltzmann distribution, which is challenging using conventional approaches such as simulations. In this work, we introduce surrogate model-assisted molecular dynamics (SMA-MD), a new procedure to sample the equilibrium ensemble of molecules. First, SMA-MD leverages deep generative models to enhance the sampling of slow degrees of freedom. Subsequently, the generated ensemble undergoes statistical reweighting, followed by short simulations. Our empirical results show that SMA-MD generates more diverse and lower energy ensembles than conventional MD simulations. Furthermore, we showcase the application of SMA-MD for the computation of thermodynamical properties by estimating implicit solvation free energies.

中文翻译：

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通过替代模型辅助分子动力学生成小分子构象系综

热力学性质的准确预测在药物发现和材料设计等各个领域至关重要。该任务依赖于从底层玻尔兹曼分布中采样，这对于使用模拟等传统方法来说具有挑战性。在这项工作中，我们引入了替代模型辅助分子动力学（SMA-MD），这是一种对分子平衡系综进行采样的新程序。首先，SMA-MD 利用深度生成模型来增强慢自由度的采样。随后，生成的集合进行统计重新加权，然后进行简短的模拟。我们的实证结果表明，SMA-MD 比传统的 MD 模拟产生更多样且能量更低的系综。此外，我们还展示了 SMA-MD 通过估计隐式溶剂化自由能来计算热力学性质的应用。