部分电荷预测的图网络
Graph Nets for Partial Charge Prediction
摘要：
原子部分电荷是分子动力学（MD）模拟、分子力学计算和虚拟屏蔽的关键参数，因为它们决定了静电对相互作用能的贡献。然而，目前用于计算部分电荷的方法要么是缓慢的，并且与分子尺寸的比例很差（量子化学方法），要么是不可靠的（经验方法）。在这里，我们提出了一种新的电荷推导方法的基础上的图形网络-一组更新和聚合功能，操作的分子拓扑结构和传播信息-可以近似的电荷来自密度泛函理论（DFT）计算具有高精度和超过500倍的速度。
英文摘要：
Atomic partial charges are crucial parameters for Molecular Dynamics (MD) simulations, molecular mechanics calculations, and virtual screening, as they determine the electrostatic contributions to interaction energies. Current methods for calculating partial charges, however, are either slow and scale poorly with molecular size (quantum chemical methods) or unreliable (empirical methods). Here, we present a new charge derivation method based on Graph Nets---a set of update and aggregate functions that operate on molecular topologies and propagate information thereon---that could approximate charges derived from Density Functional Theory (DFT) calculations with high accuracy and an over 500-fold speed up.
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