论文标题
水在宿主 - 阵线互动中的作用
The role of water in host-guest interaction
论文作者
论文摘要
原子计算机模拟的主要应用之一是配体结合能的计算。这些计算的准确性取决于力场质量和构型抽样的彻底性。由于自由能景观中的动力学瓶颈外观频繁出现，采样是现代模拟的障碍。通常，通过增强的采样技术可以规避这种困难。通常，这些技术取决于引入适当的集体变量，这些变量旨在捕获系统的自由度。在配体绑定中，长期以来，众所周知，水起着关键作用，但事实证明，它的复杂行为很难完全捕获。在本文中，我们将机器学习与物理直觉相结合，以构建非本地和高效的水列表集体变量。我们使用它来研究Sampl5挑战中的一组主机 - 阵线系统。我们获得了高度准确的结合能，并且与实验良好一致。然后将水在结合过程中的作用进行详细分析。
One of the main applications of atomistic computer simulations is the calculation of ligand binding energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in modern simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.
