NNP/MM：利用机器学习势和分子力学加速分子动力学模拟
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
AI总结：
针对机器学习势应用于生物分子模拟时计算成本高的问题，提出结合神经网络势与分子力学的混合方法NNP/MM的优化实现，将模拟速度提升5倍，实现了此类模拟有报道以来最长的时长。
摘要：
机器学习势已成为提升生物分子模拟准确性的一种手段。然而，与传统分子力学相比，其参数数量庞大，由此产生的显著计算成本限制了它们的应用。为解决这一问题，我们提出了一种混合方法（NNP/MM）的优化实现，该方法结合了神经网络势（neural network potentials，NNP）和分子力学（molecular mechanics，MM）。该方法使用NNP对系统的一部分（例如小分子）进行建模，同时对其余系统采用MM以提高效率。通过对多种蛋白质-配体复合物进行分子动力学（molecular dynamics，MD）模拟，以及对一种配体进行元动力学（metadynamics，MTD）模拟，我们展示了所实现的NNP/MM的性能。它使模拟速度提升了5倍，并实现了每个复合物合计1微秒的采样，这是此类模拟中有报道以来最长的模拟时长。
英文摘要：
Machine learning potentials have emerged as a means to enhance the accuracy of biomolecular simulations. However, their application is constrained by the significant computational cost arising from the vast number of parameters compared to traditional molecular mechanics. To tackle this issue, we introduce an optimized implementation of the hybrid method (NNP/MM), which combines neural network potentials (NNP) and molecular mechanics (MM). This approach models a portion of the system, such as a small molecule, using NNP while employing MM for the remaining system to boost efficiency. By conducting molecular dynamics (MD) simulations on various protein-ligand complexes and metadynamics (MTD) simulations on a ligand, we showcase the capabilities of our implementation of NNP/MM. It has enabled us to increase the simulation speed by 5 times and achieve a combined sampling of one microsecond for each complex, marking the longest simulations ever reported for this class of simulation.
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