论文标题
迈向与实验性可观察物相匹配的经验力场
Towards empirical force fields that match experimental observables
论文作者
论文摘要
传统上，基于参考量子化学数据和对小片段获得的实验信息的混合物，生物分子力场是传统上得出的。但是，在实现千古化采样的较大系统上运行大量分子动力学模拟的可能性是为直接使用此类仿真以及在大分子系统上获得的溶液实验的方式铺平了道路。最近，引入了许多方法来自动化此方法。在这里，我们回顾这些方法，强调它们与机器学习方法的关系，并讨论该领域的开放挑战。
Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field.
