In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter.

翻译：在分子动力学模拟中，稀有事件（如蛋白质折叠）通常使用增强采样技术进行研究，其中大多数技术基于集体变量的定义，沿该变量进行加速采样。获得表达性强的集体变量至关重要，但往往因缺乏特定事件（例如从非折叠构象到折叠构象的转变）的信息而受阻。我们提出了一种免模拟的数据增强策略，利用物理启发的度量生成类似于蛋白质折叠转变的测地线插值，从而在没有真实过渡态样本的情况下提高采样效率。这些新数据可用于提高基于分类器方法的准确性。或者，可以利用插值进度参数，采用基于回归的集体变量模型学习方案。