The development of reliable and extensible molecular mechanics (MM) forcefields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

中文翻译：

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来自大规模量子化学数据的机器学习分子力学力场


可靠且可扩展的分子力学 (MM) 力场（表征分子系统势能面的快速经验模型）的开发对于生物分子模拟和计算机辅助药物设计是不可或缺的。在这里，我们介绍了一种通用且可扩展的机器学习MM力场espaloma-0.3，以及使用图神经网络的端到端可微分框架，以克服传统基于规则的方法的局限性。 espaloma-0.3 在单个 GPU 日内进行训练，以适应超过 110 万次能量和力计算的大型且多样化的量子化学数据集，再现了与药物发现高度相关的化学领域的量子化学能量特性，包括小分子、肽和核酸酸。此外，该力场保持了小分子的量子化学能量最小化的几何形状，并保留了肽和折叠蛋白质的凝聚相特性，自洽地参数化蛋白质和配体以产生稳定的模拟，从而对结合自由能进行高度准确的预测。该方法显示出作为系统地构建更准确的力场的重要前景，这些力场可以轻松扩展到感兴趣的新化学领域。