论文标题
NNP/mm：具有机器学习势和分子力学的加速分子动力学模拟
NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic
论文作者
论文摘要
机器学习潜力已成为提高生物分子模拟准确性的一种手段。但是，与传统分子力学相比，其应用受到大量参数产生的大量计算成本的限制。为了解决此问题，我们引入了混合方法（NNP/MM）的优化实施，该方法结合了神经网络电位（NNP）和分子力学（MM）。这种方法使用NNP对系统的一部分（例如小分子）进行建模，同时使用MM用于剩余系统以提高效率。通过对配体的各种蛋白质配体复合物和元动力学（MTD）模拟进行分子动力学（MD）模拟，我们展示了我们实施NNP/mm的能力。它使我们能够将仿真速度提高5次，并为每个复合物进行一个微秒的合并采样，这标志着此类仿真有史以来最长的模拟。
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.
